case study related to decision making

• +1 0234 56 7890
• [email protected]

Effective Decision-Making: A Case Study

Effective decision-making:, leading an organization through timely and impactful action.

Senior leaders at a top New England insurance provider need to develop the skills and behaviors for better, faster decision-making. This virtually delivered program spans four half-day sessions and includes individual assignments, facilitator-led presentations, and simulation decision-making. Over the past two months, this program touched over 100 leaders, providing them with actionable models and frameworks to use back on the job.

For one of New England’s most iconic insurers, senior leaders are challenged to make timely, effective decisions. These leaders face decisions on three levels: ones they translate to their teams, ones they make themselves, and ones they influence. But in a quickly changing, highly regulated market, risk aversion can lead to slow and ineffective decisions. How can senior leaders practice in a safe environment the quick, yet informed, decision-making necessary for the job while simultaneously learning new models and techniques — and without the learning experience burdening their precious time?

The Effective Decision-Making program was artfully designed to immerse senior leaders in 16 hours of hands-on experience, including reflection and feedback activities, applicable exercises, supporting content, and participation in a business simulation to practice the core content of the program. Participants work together in small groups to complete these activities within a limited time frame, replicating the work environment in which these leaders must succeed. Continuous reflection and group discussion around results create real-time learning for leaders. Application exercises then facilitate the simulation experience and their work back on the job. The program employs a variety of learning methodologies, including:

• Individual assignments that incorporate content and frameworks designed to develop effective decision-making skills.
• Guided reflection activities to encourage self-awareness and commitments for action.
• Large group conversations — live discussions focused on peer input around key learning points.
• Small group activities, including virtual role plays designed to build critical interpersonal and leadership skills.
• A dynamic business simulation in which participants are charged with translating, making, and influencing difficult decisions.
• Facilitator-led discussions and presentations.

Learning Objectives

Participants develop and improve skills to:

• Cultivate a leadership mindset that empowers, inspires, and challenges others.
• Translate decisions for stronger team alignment and performance.
• Make better decisions under pressure.
• Influence individuals across the organization.
• Better understand how one’s leadership actions impact business results

Design Highlights

Program agenda.

As a result of the COVID-19 pandemic and the need for social distancing, this program was delivered virtually. However, this didn't preclude the need to give leaders an opportunity to connect with, and learn from, one another. In response to those needs, Insight Experience developed a fully remote, yet highly interactive, offering delivered over four half-day sessions.

Interactive Virtual Learning Format

Effective Decision-Making was designed to promote both individual and group activities and reflection. Participants access the program via a video-conferencing platform that allows them to work together both in large and small groups. Learning content and group discussions are done as one large group, enabling consistency in learning and opportunities to hear from all participants. The business simulation decision-making and reflection activities are conducted in small groups, allowing teams to develop deeper connections and conversations.

Simulation Overview

Participants assume the role of a General Manager for InfoMaster, a message management provider. Their leadership challenge as the GM is to translate the broader IIC organizational goals into strategy for their business, support that strategy though the development of organizational capabilities and product offerings, manage multiple divisions and stakeholders, and consider their contribution and responsibility to the broader organization of which they are a part. 

Success in the simulation is based on how well teams:

• Understand and translate organizational strategy into goals and plans for their business unit.
• Align organizational initiatives and product development with broader strategies.
• Develop employee capabilities required to execute strategic goals.
• Hold stakeholders accountable to commitments and results.
• Communicate with stakeholders and involve others in plans and decision-making.
• Develop their network and their influence within IIC to help support initiatives for the organization

History and Results

Effective Decision-Making was developed in 2020 as an experience for senior-level leaders. After a successful pilot, the program was then rolled out to two more cohorts in 2021 and 2022. The senior-level leaders who participated in the program then requested we offer the same program to their direct reports. After some small adjustments to make the program more appropriate for director-level leaders, the program was launched in 2022 for approximately 100 directors.

Here is what some participants have said about this program:

• “ One of the better programs we've done here at [our organization]. Pace was very quick but content was excellent and approach made it fun .”
• “ Loved the content and the flow. Very nicely organized and managed. Thank you! ”
• “ Really enjoyed the collaborative nature of the simulation.”
• “ It was wonderful and I felt it is a great opportunity. Learnt and reinforced leadership training and what it would take to be successful.”
• “One of the best I've experienced — especially appreciated how the reality of [our organization] was incorporated and it was with similarly situated peers.”
• “This program was great! It gave good insight into how to enhance my skills as leader by adopting the leadership mindset.”
• “Loved the fast pace, having a sim group that had various backgrounds in the company and seeing the results of our decisions at the corporate level.”
• “Great program — I love the concepts highlighted during these sessions.”

Looking for results like these?

Leave comment, recent posts.

Taking Top Salespeople to Top Sales Leadership: A Case Study

Practice Makes Permanent: Microlearning on the Go (a Case Study)

Transforming Leadership to Support Significant Change: A Case Study

Leadership Foundations for Individual Contributors: A Case Study

Connecting Category Management Decisions and Business Outcomes: A Case Study

Accelerate the Implementation of Manager Expectations: A Case Study

Developing Effective Global Leaders: A Case Study

Delivering Exceptional Internal Customer Service: A Case Study

Bringing Leadership Principles to Life: A Case Study

Navigating the Organization: A Case Study

Smart. Open. Grounded. Inventive. Read our Ideas Made to Matter.

Which program is right for you?

Through intellectual rigor and experiential learning, this full-time, two-year MBA program develops leaders who make a difference in the world.

A rigorous, hands-on program that prepares adaptive problem solvers for premier finance careers.

A 12-month program focused on applying the tools of modern data science, optimization and machine learning to solve real-world business problems.

Earn your MBA and SM in engineering with this transformative two-year program.

Combine an international MBA with a deep dive into management science. A special opportunity for partner and affiliate schools only.

A doctoral program that produces outstanding scholars who are leading in their fields of research.

Bring a business perspective to your technical and quantitative expertise with a bachelor’s degree in management, business analytics, or finance.

A joint program for mid-career professionals that integrates engineering and systems thinking. Earn your master’s degree in engineering and management.

An interdisciplinary program that combines engineering, management, and design, leading to a master’s degree in engineering and management.

Executive Programs

A full-time MBA program for mid-career leaders eager to dedicate one year of discovery for a lifetime of impact.

This 20-month MBA program equips experienced executives to enhance their impact on their organizations and the world.

Non-degree programs for senior executives and high-potential managers.

A non-degree, customizable program for mid-career professionals.

Teaching Resources Library

Case studies.

The teaching business case studies available here are narratives that facilitate class discussion about a particular business or management issue. Teaching cases are meant to spur debate among students rather than promote a particular point of view or steer students in a specific direction.  Some of the case studies in this collection highlight the decision-making process in a business or management setting. Other cases are descriptive or demonstrative in nature, showcasing something that has happened or is happening in a particular business or management environment. Whether decision-based or demonstrative, case studies give students the chance to be in the shoes of a protagonist. With the help of context and detailed data, students can analyze what they would and would not do in a particular situation, why, and how.

Case Studies By Category

Do Your Students Know How to Analyze a Case—Really?

Explore more.

• Case Teaching
• Student Engagement

J ust as actors, athletes, and musicians spend thousands of hours practicing their craft, business students benefit from practicing their critical-thinking and decision-making skills. Students, however, often have limited exposure to real-world problem-solving scenarios; they need more opportunities to practice tackling tough business problems and deciding on—and executing—the best solutions.

To ensure students have ample opportunity to develop these critical-thinking and decision-making skills, we believe business faculty should shift from teaching mostly principles and ideas to mostly applications and practices. And in doing so, they should emphasize the case method, which simulates real-world management challenges and opportunities for students.

To help educators facilitate this shift and help students get the most out of case-based learning, we have developed a framework for analyzing cases. We call it PACADI (Problem, Alternatives, Criteria, Analysis, Decision, Implementation); it can improve learning outcomes by helping students better solve and analyze business problems, make decisions, and develop and implement strategy. Here, we’ll explain why we developed this framework, how it works, and what makes it an effective learning tool.

The Case for Cases: Helping Students Think Critically

Business students must develop critical-thinking and analytical skills, which are essential to their ability to make good decisions in functional areas such as marketing, finance, operations, and information technology, as well as to understand the relationships among these functions. For example, the decisions a marketing manager must make include strategic planning (segments, products, and channels); execution (digital messaging, media, branding, budgets, and pricing); and operations (integrated communications and technologies), as well as how to implement decisions across functional areas.

Faculty can use many types of cases to help students develop these skills. These include the prototypical “paper cases”; live cases , which feature guest lecturers such as entrepreneurs or corporate leaders and on-site visits; and multimedia cases , which immerse students into real situations. Most cases feature an explicit or implicit decision that a protagonist—whether it is an individual, a group, or an organization—must make.

For students new to learning by the case method—and even for those with case experience—some common issues can emerge; these issues can sometimes be a barrier for educators looking to ensure the best possible outcomes in their case classrooms. Unsure of how to dig into case analysis on their own, students may turn to the internet or rely on former students for “answers” to assigned cases. Or, when assigned to provide answers to assignment questions in teams, students might take a divide-and-conquer approach but not take the time to regroup and provide answers that are consistent with one other.

To help address these issues, which we commonly experienced in our classes, we wanted to provide our students with a more structured approach for how they analyze cases—and to really think about making decisions from the protagonists’ point of view. We developed the PACADI framework to address this need.

PACADI: A Six-Step Decision-Making Approach

The PACADI framework is a six-step decision-making approach that can be used in lieu of traditional end-of-case questions. It offers a structured, integrated, and iterative process that requires students to analyze case information, apply business concepts to derive valuable insights, and develop recommendations based on these insights.

Prior to beginning a PACADI assessment, which we’ll outline here, students should first prepare a two-paragraph summary—a situation analysis—that highlights the key case facts. Then, we task students with providing a five-page PACADI case analysis (excluding appendices) based on the following six steps.

Step 1: Problem definition. What is the major challenge, problem, opportunity, or decision that has to be made? If there is more than one problem, choose the most important one. Often when solving the key problem, other issues will surface and be addressed. The problem statement may be framed as a question; for example, How can brand X improve market share among millennials in Canada? Usually the problem statement has to be re-written several times during the analysis of a case as students peel back the layers of symptoms or causation.

Step 2: Alternatives. Identify in detail the strategic alternatives to address the problem; three to five options generally work best. Alternatives should be mutually exclusive, realistic, creative, and feasible given the constraints of the situation. Doing nothing or delaying the decision to a later date are not considered acceptable alternatives.

Step 3: Criteria. What are the key decision criteria that will guide decision-making? In a marketing course, for example, these may include relevant marketing criteria such as segmentation, positioning, advertising and sales, distribution, and pricing. Financial criteria useful in evaluating the alternatives should be included—for example, income statement variables, customer lifetime value, payback, etc. Students must discuss their rationale for selecting the decision criteria and the weights and importance for each factor.

Step 4: Analysis. Provide an in-depth analysis of each alternative based on the criteria chosen in step three. Decision tables using criteria as columns and alternatives as rows can be helpful. The pros and cons of the various choices as well as the short- and long-term implications of each may be evaluated. Best, worst, and most likely scenarios can also be insightful.

Step 5: Decision. Students propose their solution to the problem. This decision is justified based on an in-depth analysis. Explain why the recommendation made is the best fit for the criteria.

Step 6: Implementation plan. Sound business decisions may fail due to poor execution. To enhance the likeliness of a successful project outcome, students describe the key steps (activities) to implement the recommendation, timetable, projected costs, expected competitive reaction, success metrics, and risks in the plan.

“Students note that using the PACADI framework yields ‘aha moments’—they learned something surprising in the case that led them to think differently about the problem and their proposed solution.”

PACADI’s Benefits: Meaningfully and Thoughtfully Applying Business Concepts

The PACADI framework covers all of the major elements of business decision-making, including implementation, which is often overlooked. By stepping through the whole framework, students apply relevant business concepts and solve management problems via a systematic, comprehensive approach; they’re far less likely to surface piecemeal responses.

As students explore each part of the framework, they may realize that they need to make changes to a previous step. For instance, when working on implementation, students may realize that the alternative they selected cannot be executed or will not be profitable, and thus need to rethink their decision. Or, they may discover that the criteria need to be revised since the list of decision factors they identified is incomplete (for example, the factors may explain key marketing concerns but fail to address relevant financial considerations) or is unrealistic (for example, they suggest a 25 percent increase in revenues without proposing an increased promotional budget).

In addition, the PACADI framework can be used alongside quantitative assignments, in-class exercises, and business and management simulations. The structured, multi-step decision framework encourages careful and sequential analysis to solve business problems. Incorporating PACADI as an overarching decision-making method across different projects will ultimately help students achieve desired learning outcomes. As a practical “beyond-the-classroom” tool, the PACADI framework is not a contrived course assignment; it reflects the decision-making approach that managers, executives, and entrepreneurs exercise daily. Case analysis introduces students to the real-world process of making business decisions quickly and correctly, often with limited information. This framework supplies an organized and disciplined process that students can readily defend in writing and in class discussions.

PACADI in Action: An Example

Here’s an example of how students used the PACADI framework for a recent case analysis on CVS, a large North American drugstore chain.

The CVS Prescription for Customer Value*

PACADI Stage

Summary Response

How should CVS Health evolve from the “drugstore of your neighborhood” to the “drugstore of your future”?

Alternatives

A1. Kaizen (continuous improvement)

A2. Product development

A3. Market development

A4. Personalization (micro-targeting)

Criteria (include weights)

C1. Customer value: service, quality, image, and price (40%)

C2. Customer obsession (20%)

C3. Growth through related businesses (20%)

C4. Customer retention and customer lifetime value (20%)

Each alternative was analyzed by each criterion using a Customer Value Assessment Tool

Alternative 4 (A4): Personalization was selected. This is operationalized via: segmentation—move toward segment-of-1 marketing; geodemographics and lifestyle emphasis; predictive data analysis; relationship marketing; people, principles, and supply chain management; and exceptional customer service.

Implementation

Partner with leading medical school

Curbside pick-up

Pet pharmacy

E-newsletter for customers and employees

Employee incentive program

CVS beauty days

Expand to Latin America and Caribbean

Healthier/happier corner

Holiday toy drives/community outreach

*Source: A. Weinstein, Y. Rodriguez, K. Sims, R. Vergara, “The CVS Prescription for Superior Customer Value—A Case Study,” Back to the Future: Revisiting the Foundations of Marketing from Society for Marketing Advances, West Palm Beach, FL (November 2, 2018).

Results of Using the PACADI Framework

When faculty members at our respective institutions at Nova Southeastern University (NSU) and the University of North Carolina Wilmington have used the PACADI framework, our classes have been more structured and engaging. Students vigorously debate each element of their decision and note that this framework yields an “aha moment”—they learned something surprising in the case that led them to think differently about the problem and their proposed solution.

These lively discussions enhance individual and collective learning. As one external metric of this improvement, we have observed a 2.5 percent increase in student case grade performance at NSU since this framework was introduced.

Tips to Get Started

The PACADI approach works well in in-person, online, and hybrid courses. This is particularly important as more universities have moved to remote learning options. Because students have varied educational and cultural backgrounds, work experience, and familiarity with case analysis, we recommend that faculty members have students work on their first case using this new framework in small teams (two or three students). Additional analyses should then be solo efforts.

To use PACADI effectively in your classroom, we suggest the following:

Advise your students that your course will stress critical thinking and decision-making skills, not just course concepts and theory.

Use a varied mix of case studies. As marketing professors, we often address consumer and business markets; goods, services, and digital commerce; domestic and global business; and small and large companies in a single MBA course.

As a starting point, provide a short explanation (about 20 to 30 minutes) of the PACADI framework with a focus on the conceptual elements. You can deliver this face to face or through videoconferencing.

Give students an opportunity to practice the case analysis methodology via an ungraded sample case study. Designate groups of five to seven students to discuss the case and the six steps in breakout sessions (in class or via Zoom).

Ensure case analyses are weighted heavily as a grading component. We suggest 30–50 percent of the overall course grade.

Once cases are graded, debrief with the class on what they did right and areas needing improvement (30- to 40-minute in-person or Zoom session).

Encourage faculty teams that teach common courses to build appropriate instructional materials, grading rubrics, videos, sample cases, and teaching notes.

When selecting case studies, we have found that the best ones for PACADI analyses are about 15 pages long and revolve around a focal management decision. This length provides adequate depth yet is not protracted. Some of our tested and favorite marketing cases include Brand W , Hubspot , Kraft Foods Canada , TRSB(A) , and Whiskey & Cheddar .

Art Weinstein , Ph.D., is a professor of marketing at Nova Southeastern University, Fort Lauderdale, Florida. He has published more than 80 scholarly articles and papers and eight books on customer-focused marketing strategy. His latest book is Superior Customer Value—Finding and Keeping Customers in the Now Economy . Dr. Weinstein has consulted for many leading technology and service companies.

Herbert V. Brotspies , D.B.A., is an adjunct professor of marketing at Nova Southeastern University. He has over 30 years’ experience as a vice president in marketing, strategic planning, and acquisitions for Fortune 50 consumer products companies working in the United States and internationally. His research interests include return on marketing investment, consumer behavior, business-to-business strategy, and strategic planning.

John T. Gironda , Ph.D., is an assistant professor of marketing at the University of North Carolina Wilmington. His research has been published in Industrial Marketing Management, Psychology & Marketing , and Journal of Marketing Management . He has also presented at major marketing conferences including the American Marketing Association, Academy of Marketing Science, and Society for Marketing Advances.

Related Articles

We use cookies to understand how you use our site and to improve your experience, including personalizing content. Learn More . By continuing to use our site, you accept our use of cookies and revised Privacy Policy .

Decision-making approaches in process innovations: an explorative case study

Journal of Manufacturing Technology Management

ISSN : 1741-038X

Article publication date: 10 December 2020

Issue publication date: 17 December 2021

The purpose of this paper is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations.

Design/methodology/approach

This study reviews the current understanding of decision structuredness for determining a decision-making approach and conducts a case study based on an interactive research approach at a global manufacturer.

The findings show the correspondence of intuitive, normative and combined intuitive and normative decision-making approaches in relation to varying degrees of equivocality and analyzability. Accordingly, the conditions for determining a decision-making choice when implementing process innovations are revealed.

Research limitations/implications

This study contributes to increased understanding of the combined use of intuitive and normative decision making in production system design.

Practical implications

Empirical data are drawn from two projects in the heavy-vehicle industry. The study describes decisions, from start to finish, and the corresponding decision-making approaches when implementing process innovations. These findings are of value to staff responsible for the design of production systems.

Originality/value

Unlike prior conceptual studies, this study considers normative, intuitive and combined intuitive and normative decision making. In addition, this study extends the current understanding of decision structuredness and discloses the correspondence of decision-making approaches to varying degrees of equivocality and analyzability.

• Uncertainty
• Decision making
• Process innovation
• Case studies
• Production systems
• Manufacturing industry

Flores-Garcia, E. , Bruch, J. , Wiktorsson, M. and Jackson, M. (2021), "Decision-making approaches in process innovations: an explorative case study", Journal of Manufacturing Technology Management , Vol. 32 No. 9, pp. 1-25. https://doi.org/10.1108/JMTM-03-2019-0087

Emerald Publishing Limited

Copyright © 2019, Erik Flores-Garcia, Jessica Bruch, Magnus Wiktorsson and Mats Jackson

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Process innovations, which involve new or significantly improved production processes or technologies, are essential for increasing manufacturing competitiveness ( Rönnberg, 2019 ; Yu et al. , 2017 ). The benefits of successfully implementing process innovations include reducing time to market, developing strong competitive barriers and increasing market share ( Krzeminska and Eckert, 2015 ; Marzi et al. , 2017 ). However, implementing process innovations does not always lead to desirable results ( Rönnberg et al. , 2016 ; Frishammar et al. , 2011 ). Instead, literature shows that staff frequently encounter difficulties when identifying decision-making approaches during the implementation of process innovations ( Eriksson et al. , 2016 ; Terjesen and Patel, 2017 ). These difficulties originate when staff responsible for implementing process innovations face unfamiliar circumstances ( Gaubinger et al. , 2014 ; Stevens, 2014 ; Jalonen, 2011 ). In particular, staff must deal with a lack of consensus and understanding (equivocality), and absence of rules or processes facilitating the analysis of information (analyzability) ( Piening and Salge, 2015 ; Milewski et al. , 2015 ; Kurkkio et al. , 2011 ; Frishammar et al. , 2011 ).

Operations management research offers diverse decision-making approaches useful for implementing process innovations ( Gino and Pisano, 2008 ; Hämäläinen et al. , 2013 ; Mardani et al. , 2015 ). This paper focuses on normative, intuitive and mixed-method decision-making approaches. Normative decision making involves quantitative analyses based on a systematic assessment of data ( Cochran et al. , 2017 ; Battaïa et al. , 2018 ; Dudas et al. , 2014 ). Intuitive decision making uses affectively charged judgments that arise through rapid, non-conscious, holistic associations ( Elbanna et al. , 2013 ; Dane and Pratt, 2007 ). The mixed-method approach considers both quantitative data and intuition ( Saaty, 2008 ; Thakur and Mangla, 2019 ; Kubler et al. , 2016 ; Hämäläinen et al. , 2013 ). It is vital to know when each decision-making approach is most suitable ( Zack, 2001 ; Eling et al. , 2014 ). Unless decision-making approaches are aligned with their conditions of use, the results could be disappointing ( Luoma, 2016 ).

Different decision-making approaches are used to solve problems when implementing process innovations ( Bellgran and Säfsten, 2010 ; Gershwin, 2018 ). However, it remains unclear when to select a particular decision-making approach ( Calabretta et al. , 2017 ; Dane et al. , 2012 ; Luoma, 2016 ; Matzler et al. , 2014 ). Recently, it is suggested that the degree of equivocality and analyzability of a decision, the structuredness of a decision, may constitute the main criteria for determining a decision-making approach ( Julmi, 2019 ). While this work provides novel insight, two salient issues require further research. First, there is a need for empirical understanding, as current findings remain purely conceptual. For example, manufacturing companies seldom experience a black-and-white divide between equivocality and analyzability when implementing process innovations ( Parida et al. , 2017 ; Eriksson et al. , 2016 ; Zack, 2007 ). Accordingly, it is necessary to remain open to unanticipated findings and the possibility that current explanations about selecting a decision-making approach require adjustments. Second, current findings give precedence to intuitive decision making over normative or mixed approaches. Identifying when and how to use normative and mixed decision making in addition to intuition is essential for implementing process innovations in the context of increasing computational capabilities and the interconnectedness of systems ( Mikalef and Krogstie, 2018 ; Liao et al. , 2017 ; Schneider, 2018 ; Rönnberg et al. , 2018 ). Thus, the purpose of this study is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations. This study focuses on production system design, including conception and planning, because this stage contributes significantly to the performance of process innovations ( Andersen et al. , 2017 ; Rösiö and Bruch, 2018 ).

2. Frame of reference

2.1 understanding equivocality and analyzability in process innovations.

Equivocality is a central organizational challenge that negatively impacts the implementation of process innovations in manufacturing companies ( Rönnberg et al. , 2016 ; Eriksson et al. , 2016 ; Parida et al. , 2017 ). The current understanding of equivocality is grounded on organization theory ( Galbraith, 1973 ). Equivocality refers to the existence of multiple and conflicting interpretations, and is associated with problems such as a lack of consensus, understanding and confusion ( Daft and Macintosh, 1981 ; Zack, 2007 ; Zack, 2001 ; Koufteros et al. , 2005 ). Equivocality originates when individuals face new or unfamiliar situations in which additional information will not help resolve misunderstandings ( Frishammar et al. , 2011 ). Individuals may experience equivocality of varying degrees ranging from high equivocality, ambiguous unclear events with no immediate suggestions about how to move forward, to low equivocality, clearly defined situations requiring additional information ( Daft and Lengel, 1986 ). The literature suggests that to reduce equivocality, staff must engage in information processing activities that exchange subjective interpretations, form consensus and enact shared understanding ( Rönnberg et al. , 2016 ; Eriksson et al. , 2016 ; Daft and Lengel, 1986 ).

Staff responsible for implementing process innovations frequently encounter problems relating to lack of agreement or consensus, namely, equivocality ( Reichstein and Salter, 2006 ; Jalonen, 2011 ; Stevens, 2014 ). The way individuals respond to such problems is referred to as analyzability ( Daft and Lengel, 1986 ). Analyzability describes the extent to which problems or activities require objective procedures as opposed to personal judgment or experience to resolve a task ( Haußmann et al. , 2012 ; Zelt et al. , 2018 ). Similar to equivocality, analyzability is subject to varying degrees. For example, tasks lacking objectives rules and procedures are regarded as having low analyzability. Conversely, tasks including clear and objective procedures leading to a solution are considered as having high analyzability. The degree of analyzability of a task is associated with its degree of equivocality ( Daft and Lengel, 1986 ; Julmi, 2019 ; Byström, 2002 ). When a task is clear and analyzable, equivocality is low, and staff can rely on the acquisition of explicit information to answer questions. When a task is unclear and of low analyzability, equivocality is high, and staff must process information to generate consensus.

2.2 Decision-making approaches

Operations management literature offers distinct approaches to decision making relevant to implementing process innovations. A first approach involves normative decision making. Normative decision making involves a logical step-by-step analysis involving a quantitative assessment ( Mintzberg et al. , 1976 ) and requires information that is clear, objective and well defined ( Dean and Sharfman, 1996 ). Normative decision making is described as a slow and conscious process where information is logically decomposed and sequentially recombined to generate an output ( Jonassen, 2012 ; Swamidass, 1991 ; Papadakis et al. , 1998 ). The benefits of normative decision-making approaches include economizing cognitive effort, solving cognitively intractable problems, producing insight and integrating knowledge ( Liberatore and Luo, 2010 ). Criticism of the use of normative decision making extend from studies suggesting that individuals are intendedly rational, but only limitedly so ( Luoma, 2016 ; Simon, 1997 ). For example, decision makers may systematically deviate from recommendations produced by decision models ( Käki et al. , 2019 ). Normative decision making, despite its alleged drawbacks, continues to be used by organizations and has frequently led to good outcomes ( Metters et al. , 2008 ; Klein et al. , 2019 ).

A second approach includes intuitive decision making ( Bendoly et al. , 2006 ; Loch and Wu, 2007 ; Gino and Pisano, 2008 ; Elbanna et al. , 2013 ; White, 2016 ). Intuitive decision making involves affectively charged judgments that arise through rapid, non-conscious, holistic association of information ( Dane and Pratt, 2007 ). Intuitive decision making is associated with having a strong hunch or feeling of knowing what is going to occur, and can be advantageous when professionals are confronted with time pressure and possess experience in a field ( Gore and Sadler-Smith, 2011 ; Dane and Pratt, 2007 ; Bennett, 1998 ; Elbanna et al. , 2013 ; Hodgkinson et al. , 2009 ; Khatri and Ng, 2000 ). Intuitive decision making is not without drawbacks. Literature suggests that managers using intuition may ignore relevant facts, have a hard time explaining the reasons for making a choice, or produce gross misjudgments ( Dane et al. , 2012 ; Elbanna et al. , 2013 ; Dane and Pratt, 2007 ).

A third alternative includes the use of mixed decision-making approaches ( Tamura, 2005 ; Hämäläinen et al. , 2013 ). The main strength of this approach lies in reducing personal bias and allowing the comparison of dissimilar alternatives while integrating quantitative analysis ( Saaty, 2008 ). Mixed decision-making approaches provide solutions to problems involving conflicting objectives or criteria affected by uncertainty ( Kahraman et al. , 2015 ). Literature presents a variety of alternatives in relation to mixed decision-making approaches ( Mardani et al. , 2015 ), yet these have the common objective of helping deal with the evaluation, selection and prioritization of problems by imposing a disciplined methodology ( Kubler et al. , 2016 ).

2.3 Structuredness of decisions and decision making

In the past, decisions have been classified along a continuum according to their structure ( Shapiro and Spence, 1997 ). This argument maintains that a decision may range from well- to ill-structured depending on whether rules and processes can be unequivocally applied. Grounded on organization theory, recent studies propose that the structuredness of decisions may provide an indication for understanding the correspondence between the choice of a decision-making approach and its conditions of use ( Julmi, 2019 ).

Well-structured decisions include intellective tasks with a definite objective criterion of success within the definitions, rules, operations and relationships of a particular conceptual system ( Dane and Pratt, 2007 ). A well-structured decision involves rules or procedures and unequivocal interpretations that have developed over time ( March and Simon, 1993 ; Luoma, 2016 ). Therefore, it is argued that well-structured decisions relate to low equivocality and high analyzability, and that normative decision making is appropriate because of the structured rules and computable information involved.

Ill-structured decisions involve judgmental tasks where there are no objective criteria, or demonstrable solutions ( Dane and Pratt, 2007 ). Ill-structured decisions originate from novel situations that do not include widely accepted rules that may help determine the degree to which a decision is correct or biased ( Cyert and March, 1992 ; Luoma, 2016 ; Jacobides, 2007 ). Consequently, it is identified that ill-structured decisions correspond to high equivocality and low analyzability. It is suggested that staff facing ill-structured decisions adopt intuitive decision-making because intuition does not rely on rules to cope with a problem; rather, it relies on integrating information holistically into coherent patterns ( Dane and Pratt, 2007 ). Figure 1 illustrates the correspondence of decision-making approaches to the conditions of use based on the structuredness of decisions.

Conceptually, the structuredness of decisions provides a starting point to understand the correspondence of a decision-making approach to its conditions of use. However, there is a need to submit these conceptual arguments to empirical scrutiny and explore whether the degree of equivocality and analyzability provides guidance in selecting a decision-making approach when implementing process innovations. The empirical study to explore these issues is described in the following section.

3. Methodology

Prior studies have focused on explaining how to choose a decision-making approach; however, there is a need for further empirical insight. This casts doubt on the appropriateness of analysis-based research, which is better suited to evaluating well-developed hypotheses ( Johnson et al. , 2007 ; Mccutcheon and Meredith, 1993 ; Handfield and Melnyk, 1998 ). Accordingly, this study adopts a qualitative-based case study to elaborate on the current theory ( Ketokivi and Choi, 2014 ). Theory elaboration is well suited to explore an empirical context with more latitude, and conduct an in-depth investigation based on identified theoretical concepts ( Whetten, 1989 ). The choice of case study research is justified by prior studies which describe its advantages for observing and describing a complicated research phenomenon such that it conveys information in a way that quantitative data cannot ( Eisenhardt and Graebner, 2007 ; Handfield and Melnyk, 1998 ; Meredith, 1998 ; Mccutcheon and Meredith, 1993 ). In designing and conducting the case study, extant guidelines for qualitative case studies in Operations Management were followed ( Barratt et al. , 2011 ).

The focus of this study is the design of production systems. Decision making at this stage is important for achieving the desired level of competitiveness and the overall goals of implementing process innovations ( Bruch and Bellgran, 2012 ). Process innovations are frequently implemented in the form of projects ( Bellgran and Säfsten, 2010 ). Accordingly, the unit of analysis is the production system design project, and its embedded unit of analysis decisions within these projects. Given the research agenda, the decisions occurring in a production system design project are an appropriate unit of analysis. These decisions should adapt to the structure of the environment ( Gigerenzer and Gaissmaier, 2011 ), and are affected by the information processing capacities of an organization ( Matzler et al. , 2014 ).

This study uses empirical data from two production system design projects at one global manufacturing company, which we refer to as Projects A and B. While case study research at a single organization offers limited generalizability ( Ahlskog et al. , 2017 ), it allows an in-depth exploration of how decision making occurs at manufacturing companies beyond well-structured decisions ( Kihlander and Ritzén, 2012 ). The manufacturing company was selected based on theoretical sampling, with the aim of exploiting opportunities to explore a significant phenomenon under rare or extreme circumstances relevant to the study of single cases ( Yin, 2013 ; Eisenhardt and Graebner, 2007 ). In selecting a manufacturing company, the study focused on four factors associated with the competent implementation of process innovations including: large-sized firms of high capital intensity, established processes for developing production systems, continual design of new products and an emphasis on increasing flexibility of production systems ( Cabagnols and Le Bas, 2002 ; Pisano, 1997 ; Martinez-Ros, 1999 ).

Two aspects influenced the choice of projects. First, the focus was on projects implementing radical process innovations, namely, those projects involving new equipment and management practices and changes in the production processes ( Reichstein and Salter, 2006 ). These types of projects reportedly experience varying degrees of equivocality and analyzability ( Parida et al. , 2017 ; Kurkkio et al. , 2011 ; Frishammar et al. , 2011 ). In addition, radical process innovations depend on normative and intuitive decision-making approaches for their implementation ( Calabretta et al. , 2017 ), which are conditions essential to the focus of this study. Second, this study gave precedence to projects that included experienced staff responsible for implementing process innovations. Prior studies highlight that experience influences the capacity of staff to act under conditions of limited information and equivocality, and facilitates making rapid decisions in the absence of data ( Daft and Macintosh, 1981 ; Liu and Hart, 2011 ; Gershwin, 2018 ; Dane and Pratt, 2007 ). Accordingly, two projects in the heavy-vehicle industry focused on the transition from traditional production systems to multi-product production systems were considered.

One of the authors of this study is a researcher at the manufacturing company. Accordingly, this study adopts an interactive research approach ( Ellström, 2008 ), which is considered a variant of collaborative research. Interactive research is distinguished by the continuous joint learning and close collaboration between industry participants and researchers ( Svensson et al. , 2007 ; Ellström, 2008 ). Despite this close interaction, the primary focus of this study is to provide a theoretical contribution and relevant industrial results.

3.1 Description of Projects A and B

The manufacturing company is a leading producer of heavy-vehicle products with more than 14,000 employees and 13 manufacturing sites in Europe, Asia and North and Latin America. The heavy-vehicle industry is characterized by a high degree of product customization and specialized product families targeting specific markets. Manufacturers of this segment consider a wide offering of products to be a key competitive advantage. Production systems are distinguished by assembly lines that specialize in a single product family, and share little else other than the same manufacturing facility.

The manufacturing company initiated two projects, A and B, which originated from a common corporate goal of reducing time to market, manufacturing footprint, and lead time to customers, and increasing production flexibility. These projects focused on the transformation of traditional production systems to multi-product production systems. Projects A and B were considered process innovations because of their novel approach compared to traditional production in the heavy-vehicle industry, which included: standardizing product interfaces, utilizing new production processes and technologies for product assembly, redesigning facility layouts and developing internal logistic solutions. Projects A and B were considered successful because these upgraded outdated production processes and technologies increased production flexibility, reduced production unit labor cost per output, increased productivity and reduced the assembly area of the production systems. Table I describes Projects A and B, and Table II outlines the profiles of staff participating in these projects.

3.2 Data collection

Data collection took place between January 2014 and January 2016. This period comprised all activities and planning for Projects A and B. Different techniques for data collection were used including field notes, interviews and company documents to help obtain objective and reliable results ( Karlsson, 2010 ). The first author drafted field notes during 12 full-day workshops for Project A and 10 full-day workshops for B. Staff responsible for Projects A and B attended these workshops including project managers, production managers, production engineers, logistics developers, consultants and research and development personnel. These separately held workshops involved three themes. The first theme consisted of generating a common vision of the process innovations, identifying critical issues and proposing solutions to these issues. The second theme included designing, developing and deploying discrete event simulation models. The third theme focused on discussing the results of on-site tests for Projects A and B. In addition, the first author participated regularly as a passive observer in project meetings and drafted field notes, including 60 and 40 1-hour weekly meetings for Projects A and B, respectively.

The authors collected additional data based on five semi-structured interviews for Projects A and B. The interviews began with an explanation of the project, its background and goals. Staff described their professional experience and responsibilities in the project and identified the essential activities and decisions of each project. Next, they narrated the process of achieving agreement for each decision. Finally, they detailed how decisions were made including decision-making approaches, rules, processes, information and outcome. To gain a comprehensive understanding of decision making, the interviews involved staff members from different seniority levels, including project managers, production engineering managers, production engineers, logistics developers and consultants. The authors recorded and transcribed all interviews and sent all transcribed interviews to the interviewees for verification. Finally, data collection included company documents in the form of presentations, minutes and reports drafted during the projects. Table III lists the details of data collection.

3.3 Data analysis

Data analysis included an iterative comparison of the collected data and existing literature, as suggested by Yin (2013) . Following the recommendations of Miles et al. (2013) , data analysis occurred in four steps. First, collected data were concurrently selected, abbreviated and stored in a database during data collection. At this stage, salient decisions were identified for Projects A and B, and the focus was on decisions involving the commitment of resources (e.g. additional meetings, production experts or managerial discussions) leading to actions (selecting a layout, proposing a definition or selecting a group of products) as suggested in the literature ( Frishammar, 2003 ). Afterwards, staff participating in Projects A and B verified these decisions.

The second step involved systematically coding the collected data for Projects A and B. The authors jointly decided on three codes for analyzing data: equivocality, analyzability and decision-making approaches. The literature was heavily relied on to identify the equivocality and analyzability associated with a decision ( Daft and Lengel, 1986 ). High equivocality referred to multiple and conflicting interpretations and ambiguous information. Equivocal situations included partial agreement among the staff and ambiguous information. Low equivocality involved unequivocal interpretations and a lack of information. High analyzability concerned clear rules and processes, and low analyzability a lack of objective rules or rule based procedures. Staff of Projects A and B were left to operate freely when selecting decision-making approaches based on preferences or established processes operating at the manufacturing company. Importantly, no definitions of decision-making approaches were provided to the staff. Instead, the decision-making approach of each decision was identified a posteriori based on the characteristics of intuitive or normative decision making found in literature and shown in Table IV .

Third, the authors reassembled data according to the codes described above and analyzed data in two steps, as suggested by Eisenhardt (1989) . First, the author analyzed the projects separately to become acquainted with and identify patterns. Thereafter, the authors analyzed patterns across Projects A and B.

Fourth, the authors compared all the findings in a joint session with the aim of achieving a comprehensive interpretation of the study. The authors deliberated over differences of interpretation until an agreement was reached. Where there was no agreement, the authors contacted interviewees for further clarification. Finally, the authors drew conclusions and conceptualized the findings of the study. The findings were compared and related to existing theory concerning similarities, contradictions and explanations of differences ( Eisenhardt, 1989 ).

4. Empirical findings

4.1 equivocality.

What is a good assembly sequence for all these different products? You had to propose what to do, and then do it, and then show the results. It is not that you would have asked someone: Are we doing the right thing? Should we do it this way? No one really had an answer for that. (Project manager of Project A)

Each of us (project B) has worked with powertrains for a long time, but this was different. Originally we believed that it was necessary to include the vehicle transmission and an additional component in our scope. This choice was not simple because of the intrinsic differences and functionalities of each product family. In addition, we lacked experience on anything remotely similar, did not have enough information, and held different opinions on the matter. (Production engineer of Project B)

We based all the work on the assumption that there is one common assembly sequence. We regarded that as a backbone in the project. I strongly believe that if you have a common assembly sequence, it has an enormous impact on production. (Project manager of Project A)

First, we decided to do an extensive data collection. That drove the project into the wall. On a second attempt, we decided not to dig so deep into the details and focused on a holistic perspective. We went through our products looking for similarities. Based on discussions with our product and production experts, we identified 17 key components; based on these, we developed a common assembly sequence. (Production engineer of Project B)

After developing a shared understanding of a multi-product assembly, our activities focused on issues that could improve our concept. We collected and analyzed information, and compared alternatives. It was essential to know what choices brought our process innovation closer to objectives set up by management. (Logistics developer of Case A)

4.2 Analyzability

Staff experienced analyzability as a tension between two opposites. On the one hand, staff was subject to familiar circumstances, known problems or decisions encountered in the past. In these situations, they adopted standardized rules and procedures common in production system design projects at the manufacturing company: for example, processes for designing a production system, line balancing strategies or the classification of logistics parts.

On the other hand, staff faced new and unfamiliar decisions originating from the specification of the characteristics of a multi-product production system. For example, the manufacturing company possessed no procedures specifying the grouping of different product families for production in a multi-product production system. Similarly, the manufacturing company did not possess rules for identifying a best choice among alternative product groups. Staff considered both decision-making processes essential for multi-product production systems.

Once we developed a common perspective about a single assembly line, we mapped assembly times, figured out the number of stations, moved as much work as possible to sub-assembly lines, worked with logistics, material handling, kitting in line. With a common objective, it was easier for us to pinpoint what the production system would look like. (Production engineer of Project A)

4.3 Decision-making approaches

Staff of Projects A and B utilized three distinct decision-making approaches including intuitive, normative and a combination of intuitive and normative. Intuitive decision making was frequent at the start of Projects A and B, and relied on gut feeling, best knowledge and a holistic consideration of information. Intuitive decision making did not focus on detailed information. Instead, the staff integrated the results from different reports and argued for a solution based on experience or hunches. The staff utilized intuitive decision making in two distinct instances. First, staff relied on intuitive decision making during open and informal debates to achieve consensus. In these circumstances, they either generated a solution to a decision (e.g. agreeing on the importance of a common assembly sequence) or determined new rules or procedures (e.g. steps for grouping and ranking product groups). Second, they utilized intuitive decision making jointly with normative decision making, e.g. in identifying problems and proposing solutions to the production process. An additional example of the latter includes simulation models. Simulation models originally included rough assumptions and simplifications based on the intuition of experts and their general understanding of the production systems, which were increasingly completed with new information.

The results of the simulation analysis were very important to the outcome of the process innovation. This helped us understand how to eliminate variation in our production process. The simulation also helped us understand how the solutions we tested in the factory floor turned out over weeks or months across different areas. We could not have achieved this detail of understanding any other way. (Consultant of Project A)

Finally, staff jointly applied a combination of intuitive and normative decision-making approaches during Projects A and B. Joint intuitive and normative decision-making approaches were subject to the agreement of the staff, collection of data and clear rules or procedures which could be either new or established ones. Intuitive decision making could precede, follow or be used concurrently with normative decision making (e.g. when determining the advantages or trade-offs of a multi-product production system). In this example, staff utilized normative decision making (e.g. simulations) to compare the production systems of sites in North and Latin America to the multi-product production systems developed in Projects A and B. The results of this comparison were presented in workshops and face-to-face meetings. In these meetings, staff participating in Projects A and B and experts from sites in North and Latin America scrutinized the simulation results and compared them to demand forecasts, production reports and experience. This required several iterations, and the primary concern was that of earning trustworthiness from experts. Afterwards, the results of a decision were escalated to a managerial level. When determining the benefits and trade-offs of a multi-product production system, managers considered information from diverse sources – and not exclusively the results of a simulation analysis. Frequently, managers requested “what if” or sensitivity types of analysis from normative decision-making approaches. Accomplishing this required a new iteration of the steps described above. Finally, managers made decisions based on intuition, considering various sources of information holistically. Tables V and VI describe the salient decisions and equivocality, analyzability and decision making of each decision for Projects A and B.

An important observation is that decisions were subject to different degrees of equivocality and analyzability when implementing process innovations. The findings show that distinct decision-making approaches occur at different degrees of equivocality and analyzability. Understanding the correspondence of equivocality and analyzability to a decision-making choice is difficult to comprehend. Therefore, Figure 2 presents the correspondence and frequency of decision-making approaches to the degree of equivocality and analyzability in Projects A and B.

The correspondence between the degree of equivocality and analyzability of a decision and decision-making approaches is identified based on a synthesis of the choices of decision-making approaches in Projects A and B and extant literature. First, findings show that decision-making approaches were most frequently utilized in conditions of low equivocality and high analyzability. In this approach, the staff interpreted a problem unequivocally, possessed clear rules and procedures; however, they lacked information. The staff utilized three different decision-making approaches in conditions of low equivocality and high analyzability, including intuitive, normative and a combination of intuitive and normative decision making.

Our data show that staff found low equivocality and high analyzability as the only conditions suitable for normative decision making in this study. Normative decision making relied on explicit information, a sequential analysis and well-defined decisions. Staff from Projects A and B utilized normative decision making for detailed technical aspects such as evaluating layouts.

In addition, the staff made use of combined intuitive and normative decision making in conditions of low equivocality and high analyzability. Here, they utilized combined intuitive and normative decision making when facing new situations, having previously agreed on procedures for analysis (e.g. identifying vehicle modules). They utilized combined intuitive and normative decision making for high stake decisions involving an aggregation of prior activities and requiring managerial involvement (e.g. comparing a multi-product production system to existing multi-product production systems).

Finally, the staff utilized intuitive decision making in low equivocality and high analyzability when encountering situations perceived as similar to prior situations. In these instances, they relied on experience, quick decisions and a holistic association of information to produce a result (e.g. agreeing on the need for improving staff competence).

Second, Projects A and B faced conditions of low equivocality and low analyzability. Staff agreed on the nature of a problem; however, they lacked clear rules, procedures and relevant information. They judged that these conditions did not meet the criteria for the exclusive use of normative decision making. Instead, they utilized intuitive or a combination of intuitive and normative decision-making approaches. The staff applied intuitive decision making to decisions where the end goal was that of establishing rules or procedures. In these instances, they were not undecided about the goal of a decision, rather how to arrive at a solution (e.g. establishing the rules and procedures for modular assembly and performance indicators). When combining intuitive and normative decision making, they utilized intuition for agreeing on rules and procedures, associated decisions to those faced in the past, and devised steps that were understandable to others based on experience. Next, quantitative analyses were utilized to provide detailed insight, acquire information and logically decompose a problem (e.g. specifying an assembly sequence).

Third, staff of Projects A and B made decisions in a context of equivocality and high analyzability. This coincided with having clear rules and processes; however, with only a partial agreement about the information necessary to complete a task or the outcome of a decision. In these instances, the staff resorted to intuitive decision making for agreeing on the type of information necessary to complete a task. Next, they utilized normative decision making in the form of quantitative based analysis such as spread sheet calculations or simulations. Finally, they returned to intuitive decision making to arrive at a solution while considering holistic information from a variety of sources. Examples of this include proposing logistics solutions for multi-product production systems, and determining advantages and trade-offs of multi-product production systems. The findings of this study would suggest that the conditions of equivocality and high analyzability do not provide sufficient support for the use of an entirely normative decision-making approach. Empirical results suggest that applying purely intuitive decision-making approaches is undesirable. Actually, the staff recognized that decisions could not rest exclusively on hunches, experience or rapid decisions by acknowledging the need for additional information, and disputing the appropriateness of information to complete a task.

Fourth, staff of Projects A and B made decisions against a backdrop of equivocality and low analyzability. These decisions involved the lack of rules or processes and partial agreement about information necessary to complete a task. Decisions of equivocality and low analyzability were not like small differences of opinion resolved over the course of a meeting or workshop. Instead, these decisions required detailed investigation, resource commitment and weeks of deliberation. Staff in Projects A and B proceeded differently when encountering equivocality and low analyzability.

In Project A, the staff identified the logistics needs for a multi-product production system. They agreed on the need for adapting logistics capabilities; however, the information available did not correspond to the needs of a multi-product production system. They estimated logistics needs based on hunches, discussions and experience. They considered the outcome of this decision provisional and subject to increased knowledge about logistics in a multi-product production system. In Project B, the staff proposed a layout for a multi-product production system. To do so, they utilized intuitive decision making to set an initial direction. This was considered insufficient to finalize a decision, and additional information was acquired, and alternatives were judged based on normative decision making.

Findings suggest that these types of decisions are not readily solvable, and evidence a need for generating agreement about the purpose of the decision, information, rules and processes enabling a solution. Data suggest that intuitive decision making is important in enacting a shared understanding; nevertheless, committing to a decision may require the quantitative insight provided by normative decision making. Consequently, decisions experiencing equivocality and low analyzability were subject to a combined intuitive and normative decision-making approach. Examples include identifying logistics needs for multi-product production systems or proposing layouts for multi-product production systems.

Fifth, findings show that no decisions coincided with high equivocality and high analyzability, namely, multiple and conflicting interpretation, ambiguous information, and clear rules and processes. We argue that high equivocality and high analyzability present a contradiction and suggest that the incidence of decision making in these conditions may signal an error. This error may well indicate the inadequate interpretation of existing rules or processes by staff responsible for implementing process innovations.

Sixth, staff made exclusive use of intuitive decision making in decisions involving high equivocality and low analyzability. These type of decisions were characterized by the absence of objectives rules or processes, multiple and conflicting interpretations, and ambiguous information. These decisions were common in the beginning of Projects A and B, and when the staff faced decisions perceived as different from those encountered in the past. They relied on hunches, approximations or conjectures about the result of a decision to guide consensus. Additional information did not help resolve decisions in high equivocality and low analyzability: for instance, when agreeing on the definition of a powertrain across different product families. Figure 3 outlines the choice of decision-making approaches when implementing process innovations according the degree of equivocality and analyzability of decisions.

5. Discussion and implications

The purpose of this study is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations. In particular, this study focused on how the conditions of equivocality and analyzability provide guidance to the choice of a decision-making approach. Extant literature is compared to empirical findings from two projects implementing process innovations in the form of a multi-product production system in the heavy-vehicle industry. The findings of this study are particularly relevant in light of the interest from manufacturing managers and academics to better understand when and where a decision-making approach is most suitable during the implementation of process innovations.

5.1 Theoretical implications

Recent studies recommended decision-making approaches in extreme cases of problem structuredness, high equivocality and low analyzability or low equivocality and high analyzability ( Julmi, 2019 ). However, staff face varying degrees of equivocality and analyzability when implementing process innovations ( Parida et al. , 2017 ; Frishammar et al. , 2011 ). This study reveals additional combinations of equivocality and analyzability than those previously described in literature. This finding is important because it extends current understanding of decision structuredness, which thus far had been limited to presenting extreme cases, namely, well- and ill-structured decisions. In addition, this study provides empirical evidence that staff must respond to decisions at varying degrees of equivocality and analyzability when implementing process innovations. In particular, this study identified three degrees of equivocality and two of analyzability when implementing process innovations. This study highlights the need for increased understanding of equivocality and analyzability, which may help manufacturing companies avoid failed choice or erroneous approaches to decision making when implementing process innovations. This finding is important as it may help clarify the selection of decision-making approaches leading to an improved outcome ( Calabretta et al. , 2017 ; Luoma, 2016 ), a situation that is crucial for implementing process innovations ( Frishammar et al. , 2011 ; Milewski et al. , 2015 ).

Current understanding of decision structuredness argues that there are no superior decision-making approaches ( Julmi, 2019 ). Instead, a decision-making approach may be better suited to certain conditions and, under these conditions, lead to an effective outcome ( Gigerenzer and Gaissmaier, 2011 ). Our findings show that, consistent with the literature, well-structured and ill-structured decisions corresponded to normative and intuitive decision making. However, findings show differences with prior studies focused on decision structuredness and decision making. For example, staff applied intuitive decision making at varying degrees of equivocality and analyzability, combined normative and intuitive decision making not described in literature, and utilized more than one decision-making approach in three out of six combinations of equivocality and analyzability. The results of this study suggest that decision structuredness may not prescribe a decision-making approach, but may clarify the conditions in which decisions take place. This finding is important because it suggests that current understanding of decision-making choice based on extreme cases of problem structuredness, namely well- or ill-structured decisions, is insufficient to guide a choice of decision-making approach. Addressing this dearth of understanding, this study outlines the choice of decision-making approaches when implementing process innovations according the degree of equivocality and analyzability of decisions. This findings is essential as it suggests that identifying the fit of a decision-making approach to the structuredness of a problem is as important as the technical acumen, resources and experience necessary for using a particular type of decision making ( Jonassen, 2012 ; Dean and Sharfman, 1996 ).

By classifying decisions in relation to their degree of equivocality, this study shows that decisions occur more frequently in situations involving low equivocality, followed by those of high equivocality, and finally by those involving partial agreement and ambiguous information or equivocal. A higher frequency of decisions in situations of low equivocality is expected when implementing process innovations. However, an intriguing finding of this study involves the frequency in which staff made decisions in situations including multiple and conflicting interpretations and ambiguous information (e.g. high equivocality). These decisions appeared when staff identified a problem (e.g. product, production process, tools and technology, layouts, logistics), were based on intuitive decision making and defined subsequent decisions of Projects A and B. This finding is disquieting as prior studies show that manufacturing companies frequently rely on ad hoc practices when making early decisions in production system design projects ( Rösiö and Bruch, 2018 ). Similarly, the literature highlights a limited understanding of equivocality at manufacturing companies when implementing process innovations ( Parida et al. , 2017 ). Therefore, our findings give credibility to the claim that comprehension of equivocality, its reduction and the effective use of intuition may harness a competitive edge for manufacturing companies implementing process innovations ( Rönnberg et al. , 2016 ; Frishammar et al. , 2012 ).

The literature advocates the use of structured processes for implementing process innovations ( Kurkkio et al. , 2011 ). Accordingly, the need for clear rules and procedures facilitating high analyzability is essential. The results of this study show no telling difference in the frequency of decisions involving high analyzability or low analyzability in Projects A and B. Importantly, data do not indicate that staff forwent rules and processes when these were lacking. Instead, staff developed rules and processes when facing decisions not previously experienced or described in established procedures. This result is significant and suggests that the ability of staff to develop rules and processes, or procedures when facing non-recurring situations ( Luoma, 2016 ), is as likely to be necessary as that of structured processes for implementing process innovations. The development of rules and processes during the implementation of process innovations is rarely discussed in literature, and therefore constitutes a venue for future research.

Mixed decision-making approaches constitute a well-established field that may help staff arrive at decisions under uncertainty ( Kubler et al. , 2016 ). This study showed that decisions were frequently reached as a result of combined intuitive and normative decision making. However, the process for arriving at these decisions was unlike the methods used in the literature. The findings of this study suggest both the need of mixed decision-making approaches when implementing process innovations, and increased efforts to bridge the gap between academic findings and manufacturing practice.

5.2 Practical implications

The findings of this study have direct practical implications that may benefit staff and managers responsible for implementing process innovations. First, this study underscores the importance of a structured process, experienced design teams and familiarity with normative, intuitive or mixed decision making that enable the implementation of process innovations ( Rösiö and Bruch, 2018 ). However, the analysis also shows that although these concepts are necessary, they are not sufficient to successfully implement process innovations. Instead, managers must be aware of the importance of determining a decision-making approach that corresponds to the conditions of a decision. Addressing this point, this study emphasized the importance of equivocality and analyzability when determining a decision-making approach during the implementation of process innovations. Accordingly, this study underscores the importance of information processing activities, which are under prioritized or neglected because of a lack of resources or competence ( Rönnberg et al. , 2016 ; Koufteros et al. , 2005 ).

5.3 Limitations and future research

Some key limitations circumscribe this study. Like all case studies, our contributions are limited by the idiosyncrasies of the context of study ( Eisenhardt, 1989 ). This study draws data from a global manufacturing company. Undoubtedly, smaller sized manufacturing companies may have different access to staff, resources and experienced personnel when implementing process innovations. Prior studies suggest that these elements affect decision-making approaches. Therefore, validating our results against cases from varying company sizes is important. Another limitation constitutes our focus on the production of heavy vehicles and their components. A suggestion for future research includes the investigation of cases in additional context: for example, the process industry or batch production.

Process innovations concern new production processes or technologies. This study, like many other process innovation studies ( Krzeminska and Eckert, 2015 ; Marzi et al. , 2017 ), focused on new material, equipment or reengineering of operational processes. In doing so, concern stemmed from the conditions that may determine the choice of a decision-making approach. Process innovation literature reflects increasing interest in the way artificial intelligence, automation and digital technologies connected to the Internet of Things affect decision making ( Rönnberg et al. , 2018 ). While the interplay of intuitive, normative and mixed decision-making approaches is a concern of this study, technological changes enabling decision making is not. Future research could focus on conceptualizing the domain of novel digital technologies and decision making when implementing process innovations.

Choice of intuitive or normative decision making based on decision structuredness

Correspondence of decision-making approaches to degree of equivocality and analyzability in Projects A and B

Choice of decision-making approaches when implementing process innovations according to the degree of equivocality and analyzability of decisions

Description of production system design Projects A and B focused on implementing a multi-product production system as a process innovation

Profiles of staff participating in Projects A and B

Details of data collection for Projects A and B

Characteristics of intuitive and normative decision-making approaches

Description of salient decisions, equivocality, analyzability and decision making in Project A

Notes: Equivocality (HE, high equivocality; E, equivocality; LE, low equivocality), Analyzability (HA, high analyzability; LA, low analyzability)

Ahlskog , M. , Bruch , J. and Jackson , M. ( 2017 ), “ Knowledge integration in manufacturing technology development ”, Journal of Manufacturing Technology Management , Vol. 28 No. 8 , pp. 1035 - 1054 .

Andersen , A.-L. , Brunoe , T.D. , Nielsen , K. and Rösiö , C. ( 2017 ), “ Towards a generic design method for reconfigurable manufacturing systems: analysis and synthesis of current design methods and evaluation of supportive tools ”, Journal of Manufacturing Systems , Vol. 42 No. 1 , pp. 179 - 195 .

Barratt , M. , Choi , T.Y. and Li , M. ( 2011 ), “ Qualitative case studies in operations management: trends, research outcomes, and future research implications ”, Journal of Operations Management , Vol. 29 No. 4 , pp. 329 - 342 .

Battaïa , O. , Dolgui , A. , Heragu , S.S. , Meerkov , S.M. and Tiwari , M.K. ( 2018 ), “ Design for manufacturing and assembly/disassembly: joint design of products and production systems ”, International Journal of Production Research , Vol. 56 No. 24 , pp. 7181 - 7189 .

Bellgran , M. and Säfsten , K. ( 2010 ), Production Development: Design and Operation of Production Systems , Springer Science & Business Media , New York, NY .

Bendoly , E. , Donohue , K. and Schultz , K.L. ( 2006 ), “ Behavior in operations management: assessing recent findings and revisiting old assumptions ”, Journal of Operations Management , Vol. 24 No. 6 , pp. 737 - 752 .

Bennett , R.H. ( 1998 ), “ The importance of tacit knowledge in strategic deliberations and decisions ”, Management Decision , Vol. 36 No. 9 , pp. 589 - 597 .

Bruch , J. and Bellgran , M. ( 2012 ), “ Creating a competitive edge when designing production systems – facilitating the sharing of design information ”, International Journal of Services Sciences , Vol. 4 Nos 3-4 , pp. 257 - 276 .

Byström , K. ( 2002 ), “ Information and information sources in tasks of varying complexity ”, Journal of the American Society for Information Science and Technology , Vol. 53 No. 7 , pp. 581 - 591 .

Cabagnols , A. and Le Bas , C. ( 2002 ), “ Differences in the determinants of product and process innovations: the french case ”, in Kleinknecht , A. and Mohnen , P. (Eds), Innovation and Firm Performance: Econometric Explorations of Survey Data , Palgrave Macmillan UK , London , pp. 112 - 149 .

Calabretta , G. , Gemser , G. and Wijnberg , N.M. ( 2017 ), “ The interplay between intuition and rationality in strategic decision making: a paradox perspective ”, Organization Studies , Vol. 38 Nos 3-4 , pp. 365 - 401 .

Cochran , D.S. , Foley , J.T. and Bi , Z. ( 2017 ), “ Use of the manufacturing system design decomposition for comparative analysis and effective design of production systems ”, International Journal of Production Research , Vol. 55 No. 3 , pp. 870 - 890 .

Cyert , R.M. and March , J.G. ( 1992 ), A Behavioral Theory of The Firm , Blackwell , Cambridge, MA .

Daft , R.L. and Lengel , R.H. ( 1986 ), “ Organizational information requirements, media richness and structural design ”, Management Science , Vol. 32 No. 5 , pp. 554 - 571 .

Daft , R.L. and Macintosh , N.B. ( 1981 ), “ A tentative exploration into the amount and equivocality of information processing in organizational work units ”, Administrative Science Quarterly , Vol. 26 No. 2 , pp. 207 - 224 .

Dane , E. and Pratt , M.G. ( 2007 ), “ Exploring intuition and its role in managerial decision making ”, Academy of Management Review , Vol. 32 No. 1 , pp. 33 - 54 .

Dane , E. , Rockmann , K.W. and Pratt , M.G. ( 2012 ), “ When should i trust my gut? Linking domain expertise to intuitive decision-making effectiveness ”, Organizational Behavior and Human Decision Processes , Vol. 119 No. 2 , pp. 187 - 194 .

Dean , J.W. and Sharfman , M.P. ( 1996 ), “ Does decision process matter? A study of strategic decision-making effectiveness ”, Academy of Management Journal , Vol. 39 No. 2 , pp. 368 - 392 .

Dudas , C. , Ng , A.H.C. , Pehrsson , L. and Boström , H. ( 2014 ), “ Integration of data mining and multi-objective optimisation for decision support in production systems development ”, International Journal of Computer Integrated Manufacturing , Vol. 27 No. 9 , pp. 824 - 839 .

Eisenhardt , K.M. ( 1989 ), “ Building theories from case study research ”, Academy of Management Review , Vol. 14 No. 4 , pp. 532 - 550 .

Eisenhardt , K.M. and Graebner , M.E. ( 2007 ), “ Theory building from cases: opportunities and challenges ”, Academy of Management Journal , Vol. 50 No. 1 , pp. 25 - 32 .

Elbanna , S. , Child , J. and Dayan , M. ( 2013 ), “ A model of antecedents and consequences of intuition in strategic decision-making: evidence from egypt ”, Long Range Planning , Vol. 46 No. 1 , pp. 149 - 176 .

Eling , K. , Griffin , A. and Langerak , F. ( 2014 ), “ Using intuition in fuzzy front-end decision-making: a conceptual framework ”, Journal of Product Innovation Management , Vol. 31 No. 5 , pp. 956 - 972 .

Ellström , P.-E. ( 2008 ), “ Knowledge creation through interactive research: a learning approach ”, Proceedings of the ECER Conference , Jönköping .

Eriksson , P.E. , Patel , P.C. , Sjödin , D.R. , Frishammar , J. and Parida , V. ( 2016 ), “ Managing interorganizational innovation projects: Mitigating the negative effects of equivocality through knowledge search strategies ”, Long Range Planning , Vol. 49 No. 6 , pp. 691 - 705 .

Frishammar , J. ( 2003 ), “ Information use in strategic decision making ”, Management Decision , Vol. 41 No. 4 , pp. 318 - 326 .

Frishammar , J. , Florén , H. and Wincent , J. ( 2011 ), “ Beyond managing uncertainty: insights from studying equivocality in the fuzzy front end of product and process innovation projects ”, IEEE Transactions on Engineering Management , Vol. 58 No. 3 , pp. 551 - 563 .

Frishammar , J. , Kurkkio , M. , Abrahamsson , L. and Lichtenthaler , U. ( 2012 ), “ Antecedents and consequences of firm’s process innovation capability: a literature review and a conceptual framework ”, IEEE Transactions on Engineering Management , Vol. 59 No. 4 , pp. 519 - 529 .

Galbraith , J.R. ( 1973 ), Designing Complex Organizations , Addison-Wesley Longman Publishing , Reading, MA .

Gaubinger , K. , Michael , R. , Swan , S. and Werani , T. ( 2014 ), Innovation and Product Management a Holistic and Practical Approach to Uncertainty Reduction , Springer , Berlin and Heidelberg .

Gershwin , S.B. ( 2018 ), “ The future of manufacturing systems engineering ”, International Journal of Production Research , Vol. 56 Nos 1–2 , pp. 224 - 237 .

Gigerenzer , G. and Gaissmaier , W. ( 2011 ), “ Heuristic decision making ”, Annual Review of Psychology , Vol. 62 No. 1 , pp. 451 - 482 .

Gino , F. and Pisano , G. ( 2008 ), “ Toward a theory of behavioral operations ”, Manufacturing & Service Operations Management , Vol. 10 No. 4 , pp. 676 - 691 .

Gore , J. and Sadler-Smith , E. ( 2011 ), “ Unpacking intuition: a process and outcome framework ”, Review of General Psychology , Vol. 15 No. 4 , pp. 304 - 316 .

Hämäläinen , R.P. , Luoma , J. and Saarinen , E. ( 2013 ), “ On the importance of behavioral operational research: the case of understanding and communicating about dynamic systems ”, European Journal of Operational Research , Vol. 228 No. 3 , pp. 623 - 634 .

Handfield , R.B. and Melnyk , S.A. ( 1998 ), “ The scientific theory-building process: a primer using the case of tqm ”, Journal of Operations Management , Vol. 16 No. 4 , pp. 321 - 339 .

Haußmann , C. , Dwivedi , Y.K. , Venkitachalam , K. and Williams , M.D. ( 2012 ), “ A summary and review of galbraith’s organizational information processing theory ”, in Dwivedi , Y.K. , Wade , M.R. and Schneberger , S.L. (Eds), Information Systems Theory: Explaining and Predicting Our Digital Society , Vol. 2 , Springer New York , New York, NY , pp. 71 - 93 .

Hodgkinson , G.P. , Sadler-Smith , E. , Burke , L.A. , Claxton , G. and Sparrow , P.R. ( 2009 ), “ Intuition in organizations: implications for strategic management ”, Long Range Planning , Vol. 42 No. 3 , pp. 277 - 297 .

Jacobides , M.G. ( 2007 ), “ The inherent limits of organizational structure and the unfulfilled role of hierarchy: lessons from a near-war ”, Organization Science , Vol. 18 No. 3 , pp. 455 - 477 .

Jalonen , H. ( 2011 ), “ The uncertainty of innovation: a systematic review of the literature ”, Journal of Management Research , Vol. 4 No. 1 , pp. 1 - 47 .

Johnson , P. , Buehring , A. , Cassell , C. and Symon , G. ( 2007 ), “ Defining qualitative management research: an empirical investigation ”, Qualitative Research in Organizations and Management: An International Journal , Vol. 2 No. 1 , pp. 23 - 42 .

Jonassen , D.H. ( 2012 ), “ Designing for decision making ”, Educational Technology Research and Development , Vol. 60 No. 2 , pp. 341 - 359 .

Julmi , C. ( 2019 ), “ When rational decision-making becomes irrational: a critical assessment and re-conceptualization of intuition effectiveness ”, Business Research , Vol. 12 No. 1 , pp. 291 - 314 .

Kahraman , C. , Onar , S.C. and Oztaysi , B. ( 2015 ), “ Fuzzy multicriteria decision-making: a literature review ”, International Journal of Computational Intelligence Systems , Vol. 8 No. 4 , pp. 637 - 666 .

Käki , A. , Kemppainen , K. and Liesiö , J. ( 2019 ), “ What to do when decision-makers deviate from model recommendations? Empirical evidence from hydropower industry ”, European Journal of Operational Research , Vol. 278 No. 3 , pp. 869 - 882 .

Karlsson , C. ( 2010 ), Researching Operations Management , Routledge , New York, NY .

Ketokivi , M. and Choi , T. ( 2014 ), “ Renaissance of case research as a scientific method ”, Journal of Operations Management , Vol. 32 No. 5 , pp. 232 - 240 .

Khatri , N. and Ng , H.A. ( 2000 ), “ The role of intuition in strategic decision making ”, Human Relations , Vol. 53 No. 1 , pp. 57 - 86 .

Kihlander , I. and Ritzén , S. ( 2012 ), “ Compatibility before completeness – identifying intrinsic conflicts in concept decision making for technical systems ”, Technovation , Vol. 32 No. 2 , pp. 79 - 89 .

Klein , R. , Koch , S. , Steinhardt , C. and Strauss , A.K. ( 2019 ), “ A review of revenue management: recent generalizations and advances in industry applications ”, European Journal of Operational Research , available at: https://doi.org/10.1016/j.ejor.2019.06.034

Koufteros , X. , Vonderembse , M. and Jayaram , J. ( 2005 ), “ Internal and external integration for product development: the contingency effects of uncertainty, equivocality, and platform strategy ”, Decision Sciences , Vol. 36 No. 1 , pp. 97 - 133 .

Krzeminska , A. and Eckert , C. ( 2015 ), “ Complementarity of internal and external r&d: Is there a difference between product versus process innovations? ”, R and D Management , Vol. 46 No. S3 , pp. 931 - 944 .

Kubler , S. , Robert , J. , Derigent , W. , Voisin , A. and Le Traon , Y. ( 2016 ), “ A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications ”, Expert Systems with Applications , Vol. 65 , pp. 398 - 422 .

Kurkkio , M. , Frishammar , J. and Lichtenthaler , U. ( 2011 ), “ Where process development begins: a multiple case study of front end activities in process firms ”, Technovation , Vol. 31 No. 9 , pp. 490 - 504 .

Liao , Y. , Deschamps , F. , Loures , E.D.F.R. and Ramos , L.F.P. ( 2017 ), “ Past, present and future of industry 4.0 – a systematic literature review and research agenda proposal ”, International Journal of Production Research , Vol. 55 No. 12 , pp. 3609 - 3629 .

Liberatore , M.J. and Luo , W. ( 2010 ), “ The analytics movement: implications for operations research ”, INFORMS Journal on Applied Analytics , Vol. 40 No. 4 , pp. 313 - 324 .

Liu , R. and Hart , S. ( 2011 ), “ Does experience matter? – A study of knowledge processes and uncertainty reduction in solution innovation ”, Industrial Marketing Management , Vol. 40 No. 5 , pp. 691 - 698 .

Loch , C.H. and Wu , Y. ( 2007 ), “ Behavioral operations management ”, Foundations and Trends® in Technology, Information and Operations Management , Vol. 1 No. 3 , pp. 121 - 232 .

Luoma , J. ( 2016 ), “ Model-based organizational decision making: a behavioral lens ”, European Journal of Operational Research , Vol. 249 No. 3 , pp. 816 - 826 .

Mccutcheon , D.M. and Meredith , J.R. ( 1993 ), “ Conducting case study research in operations management ”, Journal of Operations Management , Vol. 11 No. 3 , pp. 239 - 256 .

March , J.G. and Simon , H.A. ( 1993 ), Organizations , Blackwell Business , Oxford .

Mardani , A. , Jusoh , A. and Zavadskas , E.K. ( 2015 ), “ Fuzzy multiple criteria decision-making techniques and applications – two decades review from 1994 to 2014 ”, Expert Systems with Applications , Vol. 42 No. 8 , pp. 4126 - 4148 .

Martinez-Ros , E. ( 1999 ), “ Explaining the decisions to carry out product and process innovations: the spanish case ”, The Journal of High Technology Management Research , Vol. 10 No. 2 , pp. 223 - 242 .

Marzi , G. , Dabić , M. , Daim , T. and Garces , E. ( 2017 ), “ Product and process innovation in manufacturing firms: a 30-year bibliometric analysis ”, Scientometrics , Vol. 113 No. 2 , pp. 673 - 704 .

Matzler , K. , Uzelac , B. and Bauer , F. ( 2014 ), “ Intuition’s value for organizational innovativeness and why managers still refrain from using it ”, Management Decision , Vol. 52 No. 3 , pp. 526 - 539 .

Meredith , J. ( 1998 ), “ Building operations management theory through case and field research ”, Journal of Operations Management , Vol. 16 No. 4 , pp. 441 - 454 .

Metters , R. , Queenan , C. , Ferguson , M. , Harrison , L. , Higbie , J. , Ward , S. , Barfield , B. , Farley , T. , Kuyumcu , H.A. and Duggasani , A. ( 2008 ), “ The ‘killer application’ of revenue management: Harrah’s cherokee casino & hotel ”, INFORMS Journal on Applied Analytics , Vol. 38 No. 3 , pp. 161 - 175 .

Mikalef , P. and Krogstie , J. ( 2018 ), Big Data Analytics As An Enabler of Process Innovation Capabilities: A Configurational Approach , Springer International Publishing , Cham , pp. 426 - 441 .

Miles , M.B. , Huberman , A.M. and Saldaña , J. ( 2013 ), Qualitative Data Analysis: A Methods Sourcebook , Sage Publications , Thousand Oaks, CA .

Milewski , S.K. , Fernandes , K.J. and Mount , M.P. ( 2015 ), “ Exploring technological process innovation from a lifecycle perspective ”, International Journal of Operations & Production Management , Vol. 35 No. 9 , pp. 1312 - 1331 .

Mintzberg , H. , Raisinghani , D. and Théorêt , A. ( 1976 ), “ The structure of ‘unstructured’ decision processes ”, Administrative Science Quarterly , Vol. 21 No. 2 , pp. 246 - 275 .

Papadakis , V.M. , Lioukas , S. and Chambers , D. ( 1998 ), “ Strategic decision-making processes: the role of management and context ”, Strategic Management Journal , Vol. 19 No. 2 , pp. 115 - 147 .

Parida , V. , Patel , P.C. , Frishammar , J. and Wincent , J. ( 2017 ), “ Managing the front-end phase of process innovation under conditions of high uncertainty ”, Quality & Quantity , Vol. 51 No. 5 , pp. 1983 - 2000 .

Piening , E.P. and Salge , T.O. ( 2015 ), “ Understanding the antecedents, contingencies, and performance implications of process innovation: a dynamic capabilities perspective ”, Journal of Product Innovation Management , Vol. 32 No. 1 , pp. 80 - 97 .

Pisano , G.P. ( 1997 ), The Development Factory: Unlocking the Potential of Process Innovation , Harvard Business Press , Boston, MA .

Reichstein , T. and Salter , A. ( 2006 ), “ Investigating the sources of process innovation among uk manufacturing firms ”, Industrial and Corporate Change , Vol. 15 No. 4 , pp. 653 - 682 .

Rönnberg , D.S. ( 2019 ), “ Knowledge processing and ecosystem co-creation for process innovation: Managing joint knowledge processing in process innovation projects ”, International Entrepreneurship and Management Journal , Vol. 15 No. 1 , pp. 135 - 162 .

Rönnberg , D.S. , Frishammar , J. and Eriksson , P.E. ( 2016 ), “ Managing uncertainty and equivocality in joint process development projects ”, Journal of Engineering and Technology Management , Vol. 39 No. 1 , pp. 13 - 25 .

Rönnberg , D.S. , Parida , V. , Leksell , M. and Petrovic , A. ( 2018 ), “ Smart factory implementation and process innovation ”, Research-Technology Management , Vol. 61 No. 5 , pp. 22 - 31 .

Rösiö , C. and Bruch , J. ( 2018 ), “ Exploring the design process of reconfigurable industrial production systems: activities, challenges, and tactics ”, Journal of Manufacturing Technology Management , Vol. 29 No. 1 , pp. 85 - 103 .

Saaty , T.L. ( 2008 ), “ Decision making with the analytic hierarchy process ”, Interntional Journal of Services Sciences , Vol. 1 No. 1 , pp. 83 - 98 .

Schneider , P. ( 2018 ), “ Managerial challenges of industry 4.0: an empirically backed research agenda for a nascent field ”, Review of Managerial Science , Vol. 12 No. 3 , pp. 803 - 848 .

Shapiro , S. and Spence , M.T. ( 1997 ), “ Managerial intuition: a conceptual and operational framework ”, Business Horizons , Vol. 40 No. 1 , pp. 63 - 68 .

Simon , H.A. ( 1997 ), Models of Bounded Rationality: Empirically Grounded Economic Reason , MIT Press , Cambridge, MA .

Stevens , E. ( 2014 ), “ Fuzzy front-end learning strategies: exploration of a high-tech company ”, Technovation , Vol. 34 No. 8 , pp. 431 - 440 .

Svensson , L. , Ellström , P.-E. and Brulin , G. ( 2007 ), “ Introduction–on interactive research ”, International Journal of Action Research , Vol. 3 No. 3 , pp. 233 - 249 .

Swamidass , P.M. ( 1991 ), “ Empirical science: new frontier in operations management research ”, Academy of Management Review , Vol. 16 No. 4 , pp. 793 - 814 .

Tamura , H. ( 2005 ), “ Behavioral models for complex decision analysis ”, European Journal of Operational Research , Vol. 166 No. 3 , pp. 655 - 665 .

Terjesen , S. and Patel , P.C. ( 2017 ), “ In search of process innovations: the role of search depth, search breadth, and the industry environment ”, Journal of Management , Vol. 43 No. 5 , pp. 1421 - 1446 .

Thakur , V. and Mangla , S.K. ( 2019 ), “ Change management for sustainability: evaluating the role of human, operational and technological factors in leading indian firms in home appliances sector ”, Journal of Cleaner Production , Vol. 213 , pp. 847 - 862 .

Whetten , D.A. ( 1989 ), “ What constitutes a theoretical contribution? ”, Academy of Management Review , Vol. 14 No. 4 , pp. 490 - 495 .

White , L. ( 2016 ), “ Behavioural operational research: towards a framework for understanding behaviour in or interventions ”, European Journal of Operational Research , Vol. 249 No. 3 , pp. 827 - 841 .

Yin , R.K. ( 2013 ), Case Study Research: Design and Methods , Sage Publications , Thousand Oaks, CA .

Yu , C. , Zhang , Z. , Lin , C. and Wu , J.Y. ( 2017 ), “ Knowledge creation process and sustainable competitive advantage: the role of technological innovation capabilities ”, Sustainability , Vol. 9 No. 12 , pp. 1 - 16 .

Zack , M.H. ( 2001 ), “ If managing knowledge is the solution, then what’s the problem? ”, in Yogesh , M. (Ed.), Knowledge Management And Business Model Innovation , IGI Global , Hershey, PA , pp. 16 - 36 .

Zack , M.H. ( 2007 ), “ The role of decision support systems in an indeterminate world ”, Decision Support Systems , Vol. 43 No. 4 , pp. 1664 - 1674 .

Zelt , S. , Schmiedel , T. and Vom Brocke , J. ( 2018 ), “ Understanding the nature of processes: an information-processing perspective ”, Business Process Management Journal , Vol. 24 No. 1 , pp. 67 - 88 .

Acknowledgements

The authors gratefully acknowledge the contributions of all the participants from the anonymous company used as a case study in this research. Financial support from the Knowledge Foundation (KKS), and the industrial graduate school “Innofacture” is also gratefully acknowledged.

Corresponding author

About the authors.

Erik Flores-Garcia is Doctoral Candidate at the Innofacture Industrial Graduate School, Mälardalen University, Sweden. His research interests include simulation, production decisions and process innovation.

Jessica Bruch is Professor in production systems at Mälardalen University, Sweden. Her research interest concerns various aspects of production development and addresses both technological and organizational aspects on the project, company and inter-organizational level.

Magnus Wiktorsson is Professor in production logistics at the Royal Institute of Technology (KTH), Sweden. His research interests include two ongoing major changes in production logistics: the digitization of all processes and the need for transformation into environmentally sustainable production.

Mats Jackson is Professor in innovative production at Jönköping University, Sweden. His research interests include flexibility of production systems, industrialization and innovation in production systems.

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

• Browse All Articles
• Newsletter Sign-Up

Judgments →

• 04 Mar 2024
• Research & Ideas

Want to Make Diversity Stick? Break the Cycle of Sameness

Whether on judicial benches or in corporate boardrooms, white men are more likely to step into roles that other white men vacate, says research by Edward Chang. But when people from historically marginalized groups land those positions, workforce diversification tends to last. Chang offers three pieces of advice for leaders striving for diversity.

• 23 May 2023

Face Value: Do Certain Physical Features Help People Get Ahead?

Society seems to reward people with particular facial features. Research by Shunyuan Zhang and colleagues uses machine learning to analyze traits that people associate with charisma. The findings highlight opportunities to enhance one's image—and challenge bias.

• 29 Sep 2022

Inclusive Leadership Advice: Get Comfortable With the Uncomfortable

People tend to seek sameness, but they can teach themselves to relish in the differences of the human experience. Francesca Gino offers these three principles from improv to anyone who's trying to lead more inclusively.

• 24 Jul 2019
• Lessons from the Classroom

Can These Business Students Motivate Londoners to Do the Right Thing?

In the Harvard Business School course Behavioral Insights, students work in the UK with psychology experts to understand what motivates consumers and workers. What they learn can help businesses of all types, says Michael Luca. Open for comment; 0 Comments.

• 05 Feb 2019
• Working Paper Summaries

Stereotypes and Belief Updating

Increasing evidence demonstrates that stereotyped beliefs drive key economic decisions. This paper shows the significant role of self-stereotyping in predicting beliefs about one’s own ability. Stereotypes do not just affect beliefs about ability when information is scarce. In fact, stereotypes color the way information is incorporated into beliefs, perpetuating initial biases.

• 30 Jul 2018

Why Ethical People Become Unethical Negotiators

You may think you are an ethical person, but self-interest can cloud your judgment when you sit down at the bargaining table, says Max Bazerman. Open for comment; 0 Comments.

• 07 Aug 2013
• What Do You Think?

Is There Still a Role for Judgment in Decision-Making?

Summing Up: Human judgment should be a part of all decisions, but play a dominant role in significantly fewer of them, according to many of Jim Heskett's readers. Is good old-fashioned intuition out of date? What do YOU think? Closed for comment; 0 Comments.

• 27 Feb 2013

Sidetracked: Why Can’t We Stick to the Plan?

In her new book, Sidetracked, behavioral scientist and professor Francesca Gino explores the unexpected forces that often keep people from following through with their plans, both professional and personal. Closed for comment; 0 Comments.

• 05 Feb 2009

Why Can’t We Figure Out How to Select Leaders?

Managers discuss their own experience in organizations in response to February's column. All good leaders teach as well as learn, says Jim Heskett. Is it possible with any degree of confidence to select people for certain leadership jobs? (Forum now closed. Next forum begins March 5.) Closed for comment; 0 Comments.

• 28 Aug 2008

How Can Decision Making Be Improved?

While scholars can describe how people make decisions, and can envision how much better decision-making could be, they still have little understanding of how to help people overcome blind spots and behave optimally. Chugh, Milkman, and Bazerman organize the scattered knowledge that judgment and decision-making scholars have amassed over several decades about how to reduce biased decision-making. Their analysis of the existing literature on improvement strategies is designed to highlight the most promising avenues for future research. Key concepts include: People put great trust in their intuition. The past 50 years of decision-making research challenges that trust. A key task for psychologists is to identify how and in what decision-making situations people should try to move from intuitive, emotional thinking to more deliberative, logical thinking. The more that researchers understand the potentially harmful effects of some biased decision-making, the more important it is to have empirically tested strategies for reaching better decisions. Closed for comment; 0 Comments.

• 03 Jan 2008

Does Judgment Trump Experience?

It's a question as relevant for business as for the U.S. presidential campaign, says HBS professor Jim Heskett. If "judgment capability" is a function of experience, what kind of experience is important? Does plenty of experience really improve judgment? Online forum now CLOSED. Closed for comment; 0 Comments.

Decision Making: a Theoretical Review

• Regular Article
• Published: 15 November 2021
• Volume 56 , pages 609–629, ( 2022 )

Cite this article

• Matteo Morelli 1 ,
• Maria Casagrande   ORCID: orcid.org/0000-0002-4430-3367 2 &
• Giuseppe Forte 1 , 3  

6471 Accesses

14 Citations

Explore all metrics

Decision-making is a crucial skill that has a central role in everyday life and is necessary for adaptation to the environment and autonomy. It is the ability to choose between two or more options, and it has been studied through several theoretical approaches and by different disciplines. In this overview article, we contend a theoretical review regarding most theorizing and research on decision-making. Specifically, we focused on different levels of analyses, including different theoretical approaches and neuropsychological aspects. Moreover, common methodological measures adopted to study decision-making were reported. This theoretical review emphasizes multiple levels of analysis and aims to summarize evidence regarding this fundamental human process. Although several aspects of the field are reported, more features of decision-making process remain uncertain and need to be clarified. Further experimental studies are necessary for understanding this process better and for integrating and refining the existing theories.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Ethical decision-making theory: an integrated approach.

In AI we trust? Perceptions about automated decision-making by artificial intelligence

Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R

André, M., Borgquist, L., Foldevi, M., & Mölstad, S. (2002). Asking for ‘rules of thumb’: a way to discover tacit knowledge in general practice. Family Practice, 19 (6), 617–22. https://doi.org/10.1093/fampra/19.6.617

Article   PubMed   Google Scholar  

Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50 (1–3), 7–15. https://doi.org/10.1016/0010-0277(94)90018-3

Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 275 (5304), 1293–5. https://doi.org/10.1126/science.275.5304.1293

Bechara, A., Damasio, H., & Damasio, A. R. (2000a). Emotion, decision making and the orbitofrontal cortex. Cerebral cortex, 10 (3), 295–307.

Bechara, A., Tranel, D., & Damasio, H. (2000b). Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. Brain, 123 (Pt 11), 2189–202. https://doi.org/10.1093/brain/123.11.2189

Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: a neural theory of economic decision. Games and Economic Behavior, 52, 336–372. https://doi.org/10.1016/j.geb.2004.06.010

Article   Google Scholar  

Blanchard, T. C., Strait, C. E., & Hayden, B. Y. (2015). Ramping ensemble activity in dorsal anterior cingulate neurons during persistent commitment to a decision. Journal of Neurophysiology, 114 (4), 2439–49. https://doi.org/10.1152/jn.00711.2015

Article   PubMed   PubMed Central   Google Scholar  

Bohanec, M. (2009). Decision making: A computer-science and information-technology viewpoint. Interdisciplinary Description of Complex Systems, 7 (2), 22–37

Google Scholar  

Brand, M., Fujiwara, E., Borsutzky, S., Kalbe, E., Kessler, J., & Markowitsch, H. J. (2005). Decision-Making deficits of korsakoff patients in a new gambling task with explicit rules: associations with executive functions. Neuropsychology, 19 (3), 267–277. https://doi.org/10.1037/0894-4105.19.3.267

Broche-Pérez, Y., Jiménez, H., & Omar-Martínez, E. (2016). Neural substrates of decision-making. Neurologia, 31 (5), 319–25. https://doi.org/10.1016/j.nrl.2015.03.001

Byrnes, J. P. (2013). The nature and development of decision-making: A self-regulation model . Psychology Press

Clark, L., & Manes, F. (2004). Social and emotional decision-making following frontal lobe injury. Neurocase, 10 (5), 398–403. https://doi.org/10.1080/13554790490882799

Cummings, J. L. (1995). Anatomic and behavioral aspects of frontal-subcortical circuits a. Annals of the New York Academy of Sciences, 769 (1), 1–14

Dale, S. (2015). Heuristics and biases: The science of decision-making. Business Information Review, 32 (2), 93–99. https://doi.org/10.1177/0266382115592536

Damasio, A. R. (1996). The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 351 (1346), 1413–20. https://doi.org/10.1098/rstb.1996.0125

Dewberry, C., Juanchich, M., & Narendran, S. (2013). Decision-making competence in everyday life: The roles of general cognitive styles, decision-making styles and personality. Personality and Individual Differences, 55 (7), 783–788. https://doi.org/10.1016/j.paid.2013.06.012

Doya, K. (2008). Modulators of decision making. Nature Neuroscience, 11 (4), 410–6. https://doi.org/10.1038/nn2077

Dunn, B. D., Dalgleish, T., & Lawrence, A. D. (2006). The somatic marker hypothesis: a critical evaluation. Neuroscience & Biobehavioral Reviews, 30 (2), 239–71. https://doi.org/10.1016/j.neubiorev.2005.07.001

Elliott, R., Rees, G., & Dolan, R. J. (1999). Ventromedial prefrontal cortex mediates guessing. Neuropsychologia, 37 (4), 403–411

Ernst, M., Bolla, K., Mouratidis, M., Contoreggi, C., Matochik, J. A., Kurian, V., et al. (2002). Decision-making in a risk-taking task: a PET study. Neuropsychopharmacology, 26 (5), 682–91. https://doi.org/10.1016/S0893-133X(01)00414-6

Ernst, M., & Paulus, M. P. (2005). Neurobiology of decision making: a selective review from a neurocognitive and clinical perspective. Biological Psychiatry, 58 (8), 597–604. https://doi.org/10.1016/j.biopsych.2005.06.004

Evans, J. S. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, 255–78. https://doi.org/10.1146/annurev.psych.59.103006.093629

Fellows, L. K. (2004). The cognitive neuroscience of human decision making: A review and conceptual framework. Behavioral & Cognitive Neuroscience Reviews, 3 (3), 159–72. https://doi.org/10.1177/1534582304273251

Fellows, L. K., & Farah, M. J. (2007). The role of ventromedial prefrontal cortex in decision making: judgment under uncertainty or judgment per se? Cerebral Cortex, 17 (11), 2669–74. https://doi.org/10.1093/cercor/bhl176

Fehr, E., & Camerer, C. F. (2007). Social neuroeconomics: the neural circuitry of social preferences. Trends in Cognitive Sciences, 11 (10), 419–27. https://doi.org/10.1016/j.tics.2007.09.002

Finucane, M. L., Alhakami, A., Slovic, P., & Johnson, S. M. (2000). The affect heuristic in judgments of risks and benefits. Journal of Behavioral Decision Making, 13, 1–17.  https://doi.org/10.1002/(SICI)1099-0771(200001/03)13:1<1::AID-BDM333>3.0.CO;2-S

Fischhoff, B. (2010). Judgment and decision making. Wiley Interdisciplinary Reviews: Cognitive Science, 1 (5), 724–735. https://doi.org/10.1002/wcs.65

Forte, G., Favieri, F., & Casagrande, M. (2019). Heart rate variability and cognitive function: a systematic review. Frontiers in Neuroscience, 13, 710

Forte, G., Morelli, M., & Casagrande, M. (2021). Heart rate variability and decision-making: autonomic responses in making decisions. Brain Sciences, 11 (2), 243

Forte, G., Favieri, F., Oliha, E. O., Marotta, A., & Casagrande, M. (2021). Anxiety and attentional processes: the role of resting heart rate variability. Brain Sciences, 11 (4), 480

Frith, C. D., & Singer, T. (2008). The role of social cognition in decision making. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 363 (1511), 3875–86. https://doi.org/10.1098/rstb.2008.0156

Galotti, K. M. (2002). Making decisions that matter: How people face important life choices . Lawrence Erlbaum Associates Publishers

Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451–82. https://doi.org/10.1146/annurev-psych-120709-145346

Gigerenzer, G., & Selten, R. (Eds.). (2001). Bounded Rationality: The Adaptive Toolbox . MIT Press

Goel, V., Gold, B., Kapur, S., & Houle, S. (1998). Neuroanatomical correlates of human reasoning. Journal of Cognitive Neuroscience, 10 (3), 293–302

Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30, 535–74. https://doi.org/10.1146/annurev.neuro.29.051605.113038

Gottlieb, J. (2007). From thought to action: the parietal cortex as a bridge between perception, action, and cognition. Neuron, 53 (1), 9–16. https://doi.org/10.1016/j.neuron.2006.12.009

Gozli, D. G. (2017). Behaviour versus performance: The veiled commitment of experimental psychology. Theory & Psychology, 27, 741–758

Gozli, D. (2019). Free Choice. Experimental Psychology and Human Agency . Springer. https://doi.org/10.1007/978-3-030-20422-8_6

Group, T. M. A. D., Fawcett, T. W., Fallenstein, B., Higginson, A. D., Houston, A. I., Mallpress, D. E., & McNamara, J. M., …. (2014). The evolution of decision rules in complex environments. Trends in Cognitive Sciences , 18 (3), 153–161

Guess, C. (2004). Decision making in individualistic and collectivistic cultures. Online Readings in Psychology and Culture , 4 (1). https://doi.org/10.9707/2307-0919.1032

Gupta, R., Koscik, T. R., Bechara, A., & Tranel, D. (2011). The amygdala and decision-making. Neuropsychologia, 49 (4), 760–6. https://doi.org/10.1016/j.neuropsychologia.2010.09.029

Heilbronner, S. R., & Hayden, B. Y. (2016). Dorsal anterior cingulate cortex: a bottom-up view. Annual Review of Neuroscience, 39, 149–70. https://doi.org/10.1146/annurev-neuro-070815-013952

Hickson, L., & Khemka, I. (2014). The psychology of decision making. International review of research in developmental disabilities (Vol 47, pp. 185–229). Academic

Johnson, J. G., & Busemeyer, J. R. (2010). Decision making under risk and uncertainty. Wiley Interdisciplinary Reviews: Cognitive Science, 1 (5), 736–749. https://doi.org/10.1002/wcs.76

Kable, J. W., & Glimcher, P. W. (2009). The neurobiology of decision: consensus and controversy. Neuron , 63 (6),733–45.  https://doi.org/10.1016/j.neuron.2009.09.003

Kahneman, D. (2003). A perspective on judgment and choice. Mapping bounded rationality. American Psychologist, 58 (9), 697–720. https://doi.org/10.1037/0003-066X.58.9.697

Kahneman, D. (2011). P ensieri lenti e veloci . Trad.it. a cura di Serra, L., Arnoldo Mondadori Editore

Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47 (2), 263–292

Kheramin, S., Body, S., Mobini, S., Ho, M. Y., Velázquez-Martinez, D. N., Bradshaw, C. M., et al. (2002). Effects of quinolinic acid-induced lesions of the orbital prefrontal cortex on inter-temporal choice: a quantitative analysis. Psychopharmacology (Berl), 165 (1), 9–17. https://doi.org/10.1007/s00213-002-1228-6

Lee, V. K., & Harris, L. T. (2013). How social cognition can inform social decision making. Frontiers in Neuroscience, 7, 259. https://doi.org/10.3389/fnins.2013.00259

Lerner, J. S., Li, Y., Valdesolo, P., & Kassam, K. S. (2015). Emotion and decision making.  Annual Review of Psychology, 66 , 799–823

Loewenstein, G., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127 (2), 267–286. https://doi.org/10.1037/0033-2909.127.2.267

Mather, M. (2006). A review of decision-making processes: weighing the risks and benefits of aging. In Carstensen, L. L., & Hartel, C. R. (Eds.), & Committee on Aging Frontiers in Social Psychology, Personality, and Adult Developmental Psychology, Board on Behavioral, Cognitive, and Sensory Sciences, When I’m 64 (pp. 145–173). National Academies Press

Mazzucchi, L. (2012). La riabilitazione neuropsicologica: Premesse teoriche e applicazioni cliniche (3rd ed.). EDRA

Mishra, S. (2014). Decision-making under risk: integrating perspectives from biology, economics, and psychology. Personality and Social Psychology Review, 18 (3), 280–307. https://doi.org/10.1177/1088868314530517

Moreira, C. (2018). Unifying decision-making: a review on evolutionary theories on rationality and cognitive biases. arXiv preprint arXiv:1811.12455

Naqvi, N., Shiv, B., & Bechara, A. (2006). The role of emotion in decision making: a cognitive neuroscience perspective. Current Directions in Psychological Science, 15 (5), 260–264. https://doi.org/10.1111/j.1467-8721.2006.00448.x

O’Doherty, J. P., Buchanan, T. W., Seymour, B., & Dolan, R. J. (2006). Predictive neural coding of reward preference involves dissociable responses in human ventral midbrain and ventral striatum. Neuron, 49 (1), 157–66. https://doi.org/10.1016/j.neuron.2005.11.014

Padoa-Schioppa, C., & Assad, J. A. (2008). The representation of economic value in the orbitofrontal cortex is invariant for changes of menu. Nature Neuroscience, 11 (1), 95–102. https://doi.org/10.1038/nn2020

Palombo, D. J., Keane, M. M., & Verfaellie, M. (2015). How does the hippocampus shape decisions? Neurobiology of Learning and Memory, 125, 93–7. https://doi.org/10.1016/j.nlm.2015.08.005

Pardo-Vazquez, J. L., Padron, I., Fernandez-Rey, J., & Acuña, C. (2011). Decision-making in the ventral premotor cortex harbinger of action. Frontiers in Integrative Neuroscience, 5, 54. https://doi.org/10.3389/fnint.2011.00054

Paulus, M. P., & Yu, A. J. (2012). Emotion and decision-making: affect-driven belief systems in anxiety and depression. Trends in Cognitive Science, 16, 476–483. https://doi.org/10.1016/j.tics.2012.07.009

Payne, J. W. (1973). Alternative approaches to decision making under risk: Moments versus risk dimensions. Psychological Bulletin, 80 (6), 439–453. https://doi.org/10.1037/h0035260

Payne, J. W., Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker . Cambridge University Press

Phelps, E. A., Lempert, K. M., & Sokol-Hessner, P. (2014). Emotion and decision making: multiple modulatory neural circuits. Annual Review of Neuroscience, 37, 263–287

Pronin, E. (2007). Perception and misperception of bias in human judgment. Trends in Cognitive Sciences, 11 (1), 37–43

Rangel, A., Camerer, C., & Read Montague, P. (2008). Neuroeconomics: The neurobiology of value-based decision-making. Nature Reviews Neuroscience, 9 (7), 545–556. https://doi.org/10.1038/nrn2357

Reyna, V. F., & Lloyd, F. J. (2006). Physician decision making and cardiac risk: Effects of knowledge, risk perception, risk tolerance, and fuzzy processing. Journal of Experimental Psychology: Applied, 12 (3), 179–195. https://doi.org/10.1037/1076-898X.12.3.179

Rilling, J. K., & Sanfey, A. G. (2011). The neuroscience of social decision-making. Annual Review of Psychology, 62, 23–48. https://doi.org/10.1146/annurev.psych.121208.131647

Robinson, D. N. (2016). Explanation and the “brain sciences". Theory & Psychology, 26 (3), 324–332

Robbins, T. W., James, M., Owen, A. M., Sahakian, B. J., McInnes, L., & Rabbitt, P. (1994). Cambridge Neuropsychological Test Automated Battery (CANTAB): a factor analytic study of a large sample of normal elderly volunteers. Dementia, 5 (5), 266–81. https://doi.org/10.1159/000106735

Rogers, R. D., Owen, A. M., Middleton, H. C., Williams, E. J., Pickard, J. D., Sahakian, B. J., et al. (1999). Choosing between small, likely rewards and large, unlikely rewards activates inferior and orbital prefrontal cortex. The Journal of Neuroscience, 19 (20), 9029–9038. https://doi.org/10.1523/JNEUROSCI.19-20-09029.1999

Rolls, E. T., & Baylis, L. L. (1994). Gustatory, olfactory, and visual convergence within the primate orbitofrontal cortex. The Journal of Neuroscience, 14 (9), 5437–52. https://doi.org/10.1523/JNEUROSCI.14-09-05437.1994

Rolls, E. T., Critchley, H. D., Browning, A. S., Hernadi, I., & Lenard, L. (1999). Responses to the sensory properties of fat of neurons in the primate orbitofrontal cortex. The Journal of Neuroscience, 19 (4), 1532–40. https://doi.org/10.1523/JNEUROSCI.19-04-01532.1999

Rosenbloom, M. H., Schmahmann, J. D., & Price, B. H. (2012). The functional neuroanatomy of decision-making. The Journal of Neuropsychiatry and Clinical Neurosciences, 24 (3), 266–77. https://doi.org/10.1176/appi.neuropsych.11060139

Rushworth, M. F., & Behrens, T. E. (2008). Choice, uncertainty and value in prefrontal and cingulate cortex. Nature Neuroscience, 11 (4), 389–97. https://doi.org/10.1038/nn2066

Sanfey, A. G. (2007). Social decision-making: insights from game theory and neuroscience. Science, 318 (5850), 598–602. https://doi.org/10.1126/science.1142996

Serra, L., Bruschini, M., Ottaviani, C., Di Domenico, C., Fadda, L., Caltagirone, C., et al. (2019). Thalamocortical disconnection affects the somatic marker and social cognition: a case report. Neurocase, 25 (1–2), 1–9. https://doi.org/10.1080/13554794.2019.1599025

Shahsavarani, A. M., & Abadi, E. A. M. (2015). The bases, principles, and methods of decision-making: A review of literature. International Journal of Medical Reviews, 2 (1), 214–225

Slovic, P., Finucane, M. L., Peters, E., & MacGregor, D. G. (2002). Rational actors or rational fools: Implications of the affect heuristic for behavioral economics. Journal of Socio-Economics, 31 (4), 329–342. https://doi.org/10.1016/S1053-5357(02)00174-9

Slovic, P., Finucane, M. L., Peters, E., & MacGregor, D. G. (2004). Risk as analysis and risk as feelings: some thoughts about affect, reason, risk, and rationality. Risk Analysis, 24, 311–322. https://doi.org/10.1111/j.0272-4332.2004.00433.x

Staerklé, C. (2015). Political Psychology. International Encyclopedia of the Social & Behavioral Sciences , 427–433. https://doi.org/10.1016/B978-0-08-097086-8.24079-8

Tremblay, S., Sharika, K. M., & Platt, M. L. (2017). Social decision-making and the brain: a comparative perspective. Trends in Cognitive Sciences, 21 (4), 265–276. https://doi.org/10.1016/j.tics.2017.01.007

Trepel, C., Fox, C. R., & Poldrack, R. A. (2005). Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk. Brain Research. Cognitive Brain Research, 23 (1), 34–50. https://doi.org/10.1016/j.cogbrainres.2005.01.016

Van Der Pligt, J. (2015). Decision making, psychology of. International Encyclopedia of the Social & Behavioral Sciences, 2 (5), 917–922. https://doi.org/10.1016/B978-0-08-097086-8.24014-2

Von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior . Princeton University Press

Weber, E. U., & Hsee, C. K. (2000). Culture and individual judgment and decision making. Applied Psychology: An International Journal, 49, 32–61. https://doi.org/10.1111/1464-0597.00005

Weller, J. A., Levin, I. P., Shiv, B., & Bechara, A. (2009). The effects of insula damage on decision-making for risky gains and losses. Society for Neuroscience, 4 (4), 347–58. https://doi.org/10.1080/17470910902934400

Williams, D. J., & Noyes, J. M. (2007). How does our perception of risk influence decision-making? Implications for the design of risk information. Theoretical Issues in Ergonomics Science, 8, 1–35. https://doi.org/10.1080/14639220500484419

Yamada, H., Inokawa, H., Matsumoto, N., Ueda, Y., & Kimura, M. (2011). Neuronal basis for evaluating selected action in the primate striatum. European Journal of Neuroscience, 34 (3), 489–506. https://doi.org/10.1111/j.1460-9568.2011.07771.x

Download references

Author information

Authors and affiliations.

Dipartimento di Psicologia, Università di Roma “Sapienza”, Via dei Marsi. 78, 00185, Rome, Italy

Matteo Morelli & Giuseppe Forte

Dipartimento di Psicologia Dinamica, Clinica e Salute, Università di Roma “Sapienza”, Via degli Apuli, 1, 00185, Rome, Italy

Maria Casagrande

Body and Action Lab, IRCCS Fondazione Santa Lucia, Rome, Italy

Giuseppe Forte

You can also search for this author in PubMed   Google Scholar

Corresponding authors

Correspondence to Maria Casagrande or Giuseppe Forte .

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Morelli, M., Casagrande, M. & Forte, G. Decision Making: a Theoretical Review. Integr. psych. behav. 56 , 609–629 (2022). https://doi.org/10.1007/s12124-021-09669-x

Download citation

Accepted : 09 November 2021

Published : 15 November 2021

Issue Date : September 2022

DOI : https://doi.org/10.1007/s12124-021-09669-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Decision making
• Neural correlates of decision making
• Decision-making tasks
• Decision-making theories
• Find a journal
• Publish with us
• Track your research

• All Headlines

Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

• Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
• Fifty four percent of raw case users came from outside the U.S..
• The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
• Twenty-six of the cases in the list are raw cases.
• A third of the cases feature a woman protagonist.
• Orders for Yale SOM case studies increased by almost 50% compared to 2020.
• The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

What the Case Study Method Really Teaches

• Nitin Nohria

Seven meta-skills that stick even if the cases fade from memory.

It’s been 100 years since Harvard Business School began using the case study method. Beyond teaching specific subject matter, the case study method excels in instilling meta-skills in students. This article explains the importance of seven such skills: preparation, discernment, bias recognition, judgement, collaboration, curiosity, and self-confidence.

During my decade as dean of Harvard Business School, I spent hundreds of hours talking with our alumni. To enliven these conversations, I relied on a favorite question: “What was the most important thing you learned from your time in our MBA program?”

• Nitin Nohria is the George F. Baker Professor of Business Administration, Distinguished University Service Professor, and former dean of Harvard Business School.

Partner Center

McCombs School of Business

• Español ( Spanish )

Videos Concepts Unwrapped View All 36 short illustrated videos explain behavioral ethics concepts and basic ethics principles. Concepts Unwrapped: Sports Edition View All 10 short videos introduce athletes to behavioral ethics concepts. Ethics Defined (Glossary) View All 58 animated videos - 1 to 2 minutes each - define key ethics terms and concepts. Ethics in Focus View All One-of-a-kind videos highlight the ethical aspects of current and historical subjects. Giving Voice To Values View All Eight short videos present the 7 principles of values-driven leadership from Gentile's Giving Voice to Values. In It To Win View All A documentary and six short videos reveal the behavioral ethics biases in super-lobbyist Jack Abramoff's story. Scandals Illustrated View All 30 videos - one minute each - introduce newsworthy scandals with ethical insights and case studies. Video Series

Case Studies UT Star Icon

Case Studies

More than 70 cases pair ethics concepts with real world situations. From journalism, performing arts, and scientific research to sports, law, and business, these case studies explore current and historic ethical dilemmas, their motivating biases, and their consequences. Each case includes discussion questions, related videos, and a bibliography.

A Million Little Pieces

James Frey’s popular memoir stirred controversy and media attention after it was revealed to contain numerous exaggerations and fabrications.

Abramoff: Lobbying Congress

Super-lobbyist Abramoff was caught in a scheme to lobby against his own clients. Was a corrupt individual or a corrupt system – or both – to blame?

Apple Suppliers & Labor Practices

Is tech company Apple, Inc. ethically obligated to oversee the questionable working conditions of other companies further down their supply chain?

Approaching the Presidency: Roosevelt & Taft

Some presidents view their responsibilities in strictly legal terms, others according to duty. Roosevelt and Taft took two extreme approaches.

Appropriating “Hope”

Fairey’s portrait of Barack Obama raised debate over the extent to which an artist can use and modify another’s artistic work, yet still call it one’s own.

Arctic Offshore Drilling

Competing groups frame the debate over oil drilling off Alaska’s coast in varying ways depending on their environmental and economic interests.

Banning Burkas: Freedom or Discrimination?

The French law banning women from wearing burkas in public sparked debate about discrimination and freedom of religion.

Birthing Vaccine Skepticism

Wakefield published an article riddled with inaccuracies and conflicts of interest that created significant vaccine hesitancy regarding the MMR vaccine.

Blurred Lines of Copyright

Marvin Gaye’s Estate won a lawsuit against Robin Thicke and Pharrell Williams for the hit song “Blurred Lines,” which had a similar feel to one of his songs.

Bullfighting: Art or Not?

Bullfighting has been a prominent cultural and artistic event for centuries, but in recent decades it has faced increasing criticism for animal rights’ abuse.

Buying Green: Consumer Behavior

Do purchasing green products, such as organic foods and electric cars, give consumers the moral license to indulge in unethical behavior?

Cadavers in Car Safety Research

Engineers at Heidelberg University insist that the use of human cadavers in car safety research is ethical because their research can save lives.

Cardinals’ Computer Hacking

St. Louis Cardinals scouting director Chris Correa hacked into the Houston Astros’ webmail system, leading to legal repercussions and a lifetime ban from MLB.

Cheating: Atlanta’s School Scandal

Teachers and administrators at Parks Middle School adjust struggling students’ test scores in an effort to save their school from closure.

Cheating: Sign-Stealing in MLB

The Houston Astros’ sign-stealing scheme rocked the baseball world, leading to a game-changing MLB investigation and fallout.

Cheating: UNC’s Academic Fraud

UNC’s academic fraud scandal uncovered an 18-year scheme of unchecked coursework and fraudulent classes that enabled student-athletes to play sports.

Cheney v. U.S. District Court

A controversial case focuses on Justice Scalia’s personal friendship with Vice President Cheney and the possible conflict of interest it poses to the case.

Christina Fallin: “Appropriate Culturation?”

After Fallin posted a picture of herself wearing a Plain’s headdress on social media, uproar emerged over cultural appropriation and Fallin’s intentions.

Climate Change & the Paris Deal

While climate change poses many abstract problems, the actions (or inactions) of today’s populations will have tangible effects on future generations.

Cover-Up on Campus

While the Baylor University football team was winning on the field, university officials failed to take action when allegations of sexual assault by student athletes emerged.

Covering Female Athletes

Sports Illustrated stirs controversy when their cover photo of an Olympic skier seems to focus more on her physical appearance than her athletic abilities.

Covering Yourself? Journalists and the Bowl Championship

Can news outlets covering the Bowl Championship Series fairly report sports news if their own polls were used to create the news?

Cyber Harassment

After a student defames a middle school teacher on social media, the teacher confronts the student in class and posts a video of the confrontation online.

Defending Freedom of Tweets?

Running back Rashard Mendenhall receives backlash from fans after criticizing the celebration of the assassination of Osama Bin Laden in a tweet.

Dennis Kozlowski: Living Large

Dennis Kozlowski was an effective leader for Tyco in his first few years as CEO, but eventually faced criminal charges over his use of company assets.

Digital Downloads

File-sharing program Napster sparked debate over the legal and ethical dimensions of downloading unauthorized copies of copyrighted music.

Dr. V’s Magical Putter

Journalist Caleb Hannan outed Dr. V as a trans woman, sparking debate over the ethics of Hannan’s reporting, as well its role in Dr. V’s suicide.

East Germany’s Doping Machine

From 1968 to the late 1980s, East Germany (GDR) doped some 9,000 athletes to gain success in international athletic competitions despite being aware of the unfortunate side effects.

Ebola & American Intervention

Did the dispatch of U.S. military units to Liberia to aid in humanitarian relief during the Ebola epidemic help or hinder the process?

Edward Snowden: Traitor or Hero?

Was Edward Snowden’s release of confidential government documents ethically justifiable?

Ethical Pitfalls in Action

Why do good people do bad things? Behavioral ethics is the science of moral decision-making, which explores why and how people make the ethical (and unethical) decisions that they do.

Ethical Use of Home DNA Testing

The rising popularity of at-home DNA testing kits raises questions about privacy and consumer rights.

Flying the Confederate Flag

A heated debate ensues over whether or not the Confederate flag should be removed from the South Carolina State House grounds.

Freedom of Speech on Campus

In the wake of racially motivated offenses, student protests sparked debate over the roles of free speech, deliberation, and tolerance on campus.

Freedom vs. Duty in Clinical Social Work

What should social workers do when their personal values come in conflict with the clients they are meant to serve?

Full Disclosure: Manipulating Donors

When an intern witnesses a donor making a large gift to a non-profit organization under misleading circumstances, she struggles with what to do.

Gaming the System: The VA Scandal

The Veterans Administration’s incentives were meant to spur more efficient and productive healthcare, but not all administrators complied as intended.

German Police Battalion 101

During the Holocaust, ordinary Germans became willing killers even though they could have opted out from murdering their Jewish neighbors.

Head Injuries & American Football

Many studies have linked traumatic brain injuries and related conditions to American football, creating controversy around the safety of the sport.

Head Injuries & the NFL

American football is a rough and dangerous game and its impact on the players’ brain health has sparked a hotly contested debate.

Healthcare Obligations: Personal vs. Institutional

A medical doctor must make a difficult decision when informing patients of the effectiveness of flu shots while upholding institutional recommendations.

High Stakes Testing

In the wake of the No Child Left Behind Act, parents, teachers, and school administrators take different positions on how to assess student achievement.

In-FUR-mercials: Advertising & Adoption

When the Lied Animal Shelter faces a spike in animal intake, an advertising agency uses its moral imagination to increase pet adoptions.

Krogh & the Watergate Scandal

Egil Krogh was a young lawyer working for the Nixon Administration whose ethics faded from view when asked to play a part in the Watergate break-in.

Limbaugh on Drug Addiction

Radio talk show host Rush Limbaugh argued that drug abuse was a choice, not a disease. He later became addicted to painkillers.

U.S. Olympic swimmer Ryan Lochte’s “over-exaggeration” of an incident at the 2016 Rio Olympics led to very real consequences.

Meet Me at Starbucks

Two black men were arrested after an employee called the police on them, prompting Starbucks to implement “racial-bias” training across all its stores.

Myanmar Amber

Buying amber could potentially fund an ethnic civil war, but refraining allows collectors to acquire important specimens that could be used for research.

Negotiating Bankruptcy

Bankruptcy lawyer Gellene successfully represented a mining company during a major reorganization, but failed to disclose potential conflicts of interest.

Pao & Gender Bias

Ellen Pao stirred debate in the venture capital and tech industries when she filed a lawsuit against her employer on grounds of gender discrimination.

Pardoning Nixon

One month after Richard Nixon resigned from the presidency, Gerald Ford made the controversial decision to issue Nixon a full pardon.

Patient Autonomy & Informed Consent

Nursing staff and family members struggle with informed consent when taking care of a patient who has been deemed legally incompetent.

Prenatal Diagnosis & Parental Choice

Debate has emerged over the ethics of prenatal diagnosis and reproductive freedom in instances where testing has revealed genetic abnormalities.

Reporting on Robin Williams

After Robin Williams took his own life, news media covered the story in great detail, leading many to argue that such reporting violated the family’s privacy.

Responding to Child Migration

An influx of children migrants posed logistical and ethical dilemmas for U.S. authorities while intensifying ongoing debate about immigration.

Retracting Research: The Case of Chandok v. Klessig

A researcher makes the difficult decision to retract a published, peer-reviewed article after the original research results cannot be reproduced.

Sacking Social Media in College Sports

In the wake of questionable social media use by college athletes, the head coach at University of South Carolina bans his players from using Twitter.

Selling Enron

Following the deregulation of electricity markets in California, private energy company Enron profited greatly, but at a dire cost.

Snyder v. Phelps

Freedom of speech was put on trial in a case involving the Westboro Baptist Church and their protesting at the funeral of U.S. Marine Matthew Snyder.

Something Fishy at the Paralympics

Rampant cheating has plagued the Paralympics over the years, compromising the credibility and sportsmanship of Paralympian athletes.

Sports Blogs: The Wild West of Sports Journalism?

Deadspin pays an anonymous source for information related to NFL star Brett Favre, sparking debate over the ethics of “checkbook journalism.”

Stangl & the Holocaust

Franz Stangl was the most effective Nazi administrator in Poland, killing nearly one million Jews at Treblinka, but he claimed he was simply following orders.

Teaching Blackface: A Lesson on Stereotypes

A teacher was put on leave for showing a blackface video during a lesson on racial segregation, sparking discussion over how to teach about stereotypes.

The Astros’ Sign-Stealing Scandal

The Houston Astros rode a wave of success, culminating in a World Series win, but it all came crashing down when their sign-stealing scheme was revealed.

The Central Park Five

Despite the indisputable and overwhelming evidence of the innocence of the Central Park Five, some involved in the case refuse to believe it.

The CIA Leak

Legal and political fallout follows from the leak of classified information that led to the identification of CIA agent Valerie Plame.

The Collapse of Barings Bank

When faced with growing losses, investment banker Nick Leeson took big risks in an attempt to get out from under the losses. He lost.

The Costco Model

How can companies promote positive treatment of employees and benefit from leading with the best practices? Costco offers a model.

The FBI & Apple Security vs. Privacy

How can tech companies and government organizations strike a balance between maintaining national security and protecting user privacy?

The Miss Saigon Controversy

When a white actor was cast for the half-French, half-Vietnamese character in the Broadway production of Miss Saigon , debate ensued.

The Sandusky Scandal

Following the conviction of assistant coach Jerry Sandusky for sexual abuse, debate continues on how much university officials and head coach Joe Paterno knew of the crimes.

The Varsity Blues Scandal

A college admissions prep advisor told wealthy parents that while there were front doors into universities and back doors, he had created a side door that was worth exploring.

Providing radiation therapy to cancer patients, Therac-25 had malfunctions that resulted in 6 deaths. Who is accountable when technology causes harm?

Welfare Reform

The Welfare Reform Act changed how welfare operated, intensifying debate over the government’s role in supporting the poor through direct aid.

Wells Fargo and Moral Emotions

In a settlement with regulators, Wells Fargo Bank admitted that it had created as many as two million accounts for customers without their permission.

Stay Informed

Support our work.

A business journal from the Wharton School of the University of Pennsylvania

Four Pillars of Decision-driven Analytics

May 14, 2024 • 5 min read.

In an excerpt from their new book, Wharton’s Stefano Puntoni and co-author Bart De Langhe argue that the power of data can only be realized by leveraging human intelligence.

In a new book titled Decision-Driven Analytics: Leveraging Human Intelligence to Unlock the Power of Data , professors and behavioral scientists Bart De Langhe and Stefano Puntoni challenge the idea that our decisions should be driven by data. Rather, they argue that the power of data can only be realized by putting data in the background.

In this excerpt from their book, De Langhe and Puntoni draw from their own research and teaching to offer four pillars of decision-driven analytics.

In the mid-1850s, astronomers figured that the orbit of the planet Uranus was not like it should be according to the laws of physics. A French astronomer, Alexis Bouvard, thought that perhaps that was because we didn’t know about a planet further out in the solar system that exerted an influence on Uranus’s orbit. People started searching the sky for it. Soon enough, Urbain Le Verrier, another Frenchman, found the missing planet. It was named Neptune.

This was a great victory for the power of observation. Investing in data collection saved the day. It taught astronomers that the key to unraveling the mysteries of the cosmos was more and better data.

The rationale for making decisions with input from analytics rests on similar principles. Without data we navigate blind, while with data we can make decisions rooted in evidence. The implication is that good thinking means thinking with data.

The story doesn’t end here, though. An anomaly was soon observed also in the orbit of another planet: Mercury. The same Urbain Le Verrier who had found Neptune now hypothesized the existence of a missing planet lying between Mercury and the Sun. He called this missing planet Vulcan. Again, people started looking for it, only this time nobody could find it. Astronomers kept looking for Vulcan in the subsequent decades but the missing planet remained missing, and the mystery of Mercury unsolved.

The anomaly in the orbit of Mercury could be explained only half a century later. The explanation had to wait for Albert Einstein’s publication of a new theory of gravitation, called the theory of general relativity. This theory revolutionized our understanding of the universe by placing space and time in a four-dimensional continuum.

Although nobody knew that before Einstein entered the scene, all planetary orbits were in fact not conforming to Isaac Newton’s laws. Nobody knew that because the difference between the predictions of the two theories are smaller and smaller as you move away from the Sun. Only in the case of Mercury, which is the planet closest to the Sun, the curvature in space-time caused by the mass of the Sun was large enough for the divergence between the predictions based on Newton’s and Einstein’s theories to be detected by the telescopes of the time.

The mystery of Mercury was solved in a very different way from the mystery of Uranus. While the latter could be solved with better observations, the former could only be solved with better theory, by thinking without data.

Managers are like astronomers, looking to solve problems and find solutions in a complex world, where data is abundant but often hard to make sense of. The message is clear: Data and algorithms are crucial to making good decisions. But human judgment and intelligence are crucial, too.

The Four Pillars of Decision-driven Analytics

Many companies are witnessing an expanding gap between data and decisions, even with the goal of being a “data-driven organization.” The increasing complexity of data and algorithms can make it harder for decision-makers to collaborate with data analysts. For a business to thrive, it’s essential for both groups to understand and value each other’s expertise.

Many businesses find themselves overwhelmed by the sheer volume of data at their disposal. Putting decisions firmly at the center of the analytics process can be transformative. Starting with decisions and working back to the data will improve the quality of decision-making, improve the collaboration between managers and data analysts, and ultimately foster an organizational culture that is action oriented and that prizes the quality of decisions over ego or politics.

Here are the four core principles of decision-driven analytics:

• Decisions. Identify controllable, relevant decision alternatives. Consider diverse perspectives and a wide array of solutions. Prioritize feasible and impactful alternatives to achieve important business outcomes.
• Questions. Formulate precise questions that will help rank the identified decision alternatives. Ambiguous questions can lead to miscommunication and poor decisions.
• Data. Evaluate the data-generating mechanism. While Big Data can be tempting, the emphasis should be on collecting relevant data.
• Answers. When the earlier steps are done right, determining the best action becomes straightforward. Remember, acknowledging uncertainty and sidestepping overconfidence are key for informed decisions.

Decision-driven analytics is about making informed choices, not just processing data or flooding presentations with graphs. It emphasizes gleaning actionable insights from pertinent data. Embracing this approach means letting go of the notion that every data point is vital and not being distracted by the newest tools.

Data is just a means to an end. What matters is the decisions we make.

Excerpted and adapted from Decision-Driven Analytics: Leveraging Human Intelligence to Unlock the Power of Data, by Bart De Langhe and Stefano Puntoni, copyright 2024. Reprinted by permission of Wharton School Press.

More From Knowledge at Wharton

NBA Motion Tracking Data

Nba playing time statistics, 2024 kentucky derby with jeff seder, looking for more insights.

Sign up to stay informed about our latest article releases.

• Open access
• Published: 09 December 2022

Registered nurses’ views on consideration of patient perspectives during multidisciplinary team meetings in cancer care

• Linn Rosell   ORCID: orcid.org/0000-0003-2719-1866 1 , 2 ,
• Wenche Melander 1 , 3 , 4 ,
• Berit Lindahl 3 , 5 ,
• Mef Nilbert 2 &
• Marlene Malmström 3 , 4  

BMC Nursing volume  21 , Article number:  350 ( 2022 ) Cite this article

2725 Accesses

2 Citations

Metrics details

Multidisciplinary team meetings (MDTMs) represent an integral component of modern cancer care and have increasingly been implemented to ensure accurate and evidence-based treatment recommendations. During MDTMs, multiple and complex medical and patient-related information should be considered by a multi-professional team whose members contribute various perspectives. Registered nurses (RNs) are expected to share information on the patient perspective at MDTMs. However, research suggests that RNs’ contributions to case discussions are limited and that patient perspective is generally underrepresented. Our aim was to explore RNs’ views of the prerequisites for and barriers to the inclusion of the patient perspective in MDTMs in Swedish cancer care.

Data were collected from four focus group interviews with 22 RNs who worked as contact nurses in Swedish cancer care. Interviews were transcribed and analysed using inductive content analysis.

The analysis identified two categories and five subcategories. The participants presented different views and expressed ambivalence about the patient perspective in MDTMs. Subcategories were related to medical versus holistic perspectives, the added value of patient perspective, and possibilities for patient contributions. The participants also discussed prerequisites for the patient perspective to be considered in MDTM decision-making process, with subcategories related to structures promoting attention to the patient perspective and determinants of RNs’ contributions to case discussions in MDTMs.

Conclusions

This study demonstrates various views related to the patient perspective in MDTMs and identifies a great need to clarify the RN’s role. Our results indicate that if enhanced presentation of the patient perspective in MDTMs is desired, key information points and structures must be established to collect and present relevant patient-related information.

Peer Review reports

In cancer care, treatment recommendations based on multidisciplinary team meetings (MDTMs) represent a key point in the clinical trajectory and aim to ensure diagnostic accuracy and evidence-based treatment recommendations according to best practices and national guidelines [ 1 , 2 , 3 ]. The multidisciplinary team (MDT) consists of various professions and disciplines that contribute their expert skills; it generally includes surgeons, oncologists, a pathologist, a radiologist, registered nurses (RNs), and an MDT coordinator [ 4 , 5 ]. During MDTMs, multiple and complex medical and patient-related information must be considered within a short time frame. Consideration of the patient perspective is suggested to be important in order to provide treatment recommendations that have a high likelihood of being accepted and successfully implemented [ 4 , 6 ]. RNs are often thought to share information on the patient perspective in MDTMs, but several studies report limited RN contributions to MDTM case discussions and a weak focus on the patient perspective in MDTMs [ 4 , 6 , 7 , 8 ].

In the context of MDTMs, consideration of information concerning the patient perspective in clinical decision-making is multifaceted [ 9 ]. The patient perspective can include information about non-medical characteristics such as age, psychological aspects, and social factors such as family relations, profession, and preferences [ 10 ], but can also include medical information on comorbidity and physical status [ 11 ]. Several studies report that in MDTMs the biomedical perspective dominates, and less attention is given to the patient perspective [ 4 , 6 , 7 , 12 , 13 , 14 ]. This can partly be explained by a lack of strategies to collect this information before MDTMs [ 14 ]. However, limited information on the patient perspective in MDTMs may constitute a barrier to individualized treatment recommendations [ 13 , 15 , 16 ], resulting in difficulties implementing the recommendation after the MDTM [ 9 , 11 , 17 , 18 ]. At the same time, it is unclear what benefits patients the most: their perspective being included in the decision-making process, or the MDT conducting a fact-based discussion before considering patient preferences [ 9 ]. The few studies that have investigated patients’ experiences of involvement in the MDTM decision-making process identify variable patient involvement and preferences, ranging from physician-led decision-making to active patient involvement [ 19 , 20 ]. Although there are MDTM settings that include patients [ 17 , 21 ], these are rare. Diekmann et al. [ 21 ] reported varied patient experiences of MDTM participation, including both positive and negative experiences, while Lamb et al. [ 19 ] described how patients found MDTM participation intimidating, suggesting that RNs should present their perspective. As patients do not generally participate in MDTMs in Sweden, and since there is no standardized structure to collect information on the patient perspective, the term “patient perspective” is used in this study to refer to a holistic view of the unique person, including all relevant aspects that can affect the MDTM discussion and recommendations. Relevant aspects concern patient preferences, care needs, physical status, etc., and this information could be shared by the patient with the responsible physician or RN before the MDTM or be collected from medical records.

As part of the Swedish Cancer Strategy, contact nurses (CNs) were introduced in cancer care 10 years ago. The CN’s role is similar to that of the nurse navigator [ 22 ]. CNs are RNs specifically assigned to be the patient’s primary point of contact through a specific cancer trajectory. RNs (i.e., CNs in Sweden) are expected to participate in MDTMs, share information on the patient perspective, and advocate for patients’ interests in the meeting [ 4 , 6 , 8 , 9 , 14 , 22 , 23 ]. In this study, we refer to the CN as an RN. As a key member of the MDT, the RN is expected to contribute to the case discussion; however, previous studies have shown limited contributions from RNs compared to other MDTM participants [ 4 , 6 , 7 , 8 ]. Several reasons for this have been suggested, such as dominance of the medical perspective [ 24 ], resource constraints [ 25 ], and the fact that MDTM discussions are conducted at an early stage in the clinical trajectory when the RN may not have met with the patient [ 8 ]. Another possible reason may be that most communication traditionally occurs between physicians, with limited involvement of RNs in the decision-making process [ 24 ]. To gain insight into RNs’ perceptions of the patient perspective, we explored RNs’ views of the prerequisites for and barriers to the inclusion of the patient perspective in MDTMs in Swedish cancer care.

This study was conducted as a descriptive, qualitative study with an explorative design. Data were collected from focus group interviews [ 26 ]. The content was analysed using inductive content analysis [ 27 ] with the aim of describing RNs’ views of the patient perspective during the MDTMs. Reporting was conducted according to the Standards for Reporting Qualitative Research (SRQR) guidelines [ 28 ].

The MDTM is well established in Swedish cancer care, with meetings typically held on a weekly basis in local, regional, and national settings and with frequent use of video links between regional hospitals. The patient’s case is typically discussed at the time of primary diagnosis, but could also be discussed to ensure the best possible treatment in case of recurrence [ 29 ]. The MDT composition may vary somewhat depending on the diagnosis and hospital [ 2 , 3 ], and the chair is most often a physician [ 30 ]. Generally, the chair presents the case history and clinical problem, followed by MDT members contributing with their respective information and diagnostic- and treatment-related perspectives. During the case discussion, relevant treatment options are considered, resulting in treatment recommendations or potentially the need for further investigation. After the MDTM, the recommendations are communicated to the patient and, if desired, their next of kin by a physician, often together with the RN, and the final treatment decision is made in collaboration with the patient.

RNs in Sweden have a bachelor’s degree in nursing, and CNs are RNs with a specific assignment. Several higher education institutions in Sweden offer post-graduate education in palliative and oncological care as well as a specialist CN course. However, specialist education is not a requirement to work as a CN. A national mission statement for CNs in Swedish cancer care was developed in 2011 [ 31 ]. It states that CNs should participate in MDTMs, but does not specify their role or responsibility in those meetings. The assignment also specifies that CNs should: be accessible to patients; be responsible for providing information, assessing patient needs and offering support; enhance coordination; and ensure patient participation throughout the clinical trajectory [ 32 ]. In cancer care, CNs can work in medical as well as surgical departments, although the prerequisites for carrying out the assignment vary, as does the number of patient contacts.

Participants

Inclusion criteria for the study were being an RN working as a CN and having knowledge and/or experience of MDTMs. Permission to invite the RNs was received from the head of department, who also administered contact information for all RNs at the clinic in question. The participants received an invitation to participate by e-mail, including information about the study and a consent form. To ensure variation in the type of hospital (i.e., university or county hospital), speciality (i.e., medical or surgical), and clinical trajectory (i.e., breast cancer, gynaecological cancer, head and neck cancer, gastrointestinal cancer, urological cancer, and lung cancer), purposive sampling was conducted. In total 22 RNs from one university hospital and one county hospital in southern Sweden participated.

Data collection

Four focus group interviews were conducted between May and December 2018, three in the university hospital and one in the county hospital. The groups consisted of four to seven participants. All interviews took place in a remote room in the hospital and lasted between 95 minutes and 109 minutes. Before participation in the focus group interviews, participants completed an informed consent form, which was returned by post, by e-mail, or in person. A questionnaire about basic demographic information was completed before the interviews started (Table  1 ). The researchers followed a semi-structured interview guide that included open-ended questions exploring participants’ views [ 26 ]. Examples of overarching questions are: “How is the patient perspective included in MDTMs today?”; “If information on the patient perspective is included, what impact does it have?”; “What patient-related information do you think is important to include in the MDTM discussion and treatment recommendations?“; “Please describe potential barriers to and opportunities for including the patient perspective”; “Are there any potential risks related to not including the patient perspective?”; and “Do the RNs have any responsibilities related to including the patient perspective in the MDTMs?”. When required, we asked probing questions such as “Can you tell us more about that?” to explore the participants’ reasoning. The interview guide was developed by the research team based on experience of clinical practice and on literature in the research field. All focus group interviews were carried out by two researchers (LR and MM) who have training and expertise in qualitative methodologies and a good understanding of MDTMs in cancer care. The researchers took turns as moderator and assistant moderator. The moderator led the discussion, and the assistant moderator took notes and, at the end of each focus group interview, provided a brief summary of the session, on which the participants were encouraged to verify and reflect up on [ 26 ]. The interviews were recorded digitally and transcribed verbatim.

Data analysis

A qualitative content analysis [ 27 ] with an inductive approach was conducted, a method chosen because of the limited research on RNs’ perspectives of the MDTMs. The focus group interviews were analysed by three researchers (LR, WM and MM) to obtain broad and complementary analytical perspectives. The researchers independently read the verbatim transcripts several times to get a sense of the whole. Open coding was conducted during reading and the codes were grouped into subcategories. In the abstraction process that followed, the subcategories were grouped under categories based on similarities, and categories were grouped under main categories [ 27 ]. The definition of the categories was a dynamic analytical process moving back and forth between the specific and general perspectives, and the categories were developed through discussion between all the authors. The notes were used to increase the trustworthiness of the analysis. BL and MN contributed with clinical expertise, validation of the analysis, and writing the manuscript. The content analysis defined two categories and five subcategories (Table  2 ).

Different views of and ambivalence about the patient perspective in MDTMs

All participants described the biomedical perspective as dominant in MDTMs. They were ambivalent about whether or not it was possible and/or desirable to include the patient perspective to obtain a more holistic basis for discussion and treatment recommendations.

Medical versus holistic perspectives

The MDTM was often described as a quality assurance process and as a conference where the biomedical perspective dominated. The MDTM was conducted to ensure the best treatment recommendations for each patient. However, participants’ views regarding what information should be included in MDTM case discussions differed, revealing various views about the purpose of the MDTM. Some participants argued that the MDTM case presentations and discussions should focus on medical information and that patient-related information should not influence MDTM recommendations. This position is exemplified in the following quotation from a participant:

…as a patient, I need to be sure that the discussion about my illness is independent of where I come from, what I carry with me and what social network I have. (Focus group interview no. 1)

In contrast, some participants considered the patient perspective central to ensuring a holistic approach to the MDTM case discussion and to providing individualized treatment recommendations. However, the question of including the patient perspective was understood to be complex, involving reflections on content, format, and responsibilities, and it was unclear how the perspective should be included and by whom it should be shared. To provide additional information and allow consideration of other aspects, some participants suggested a broader professional participation of, for example, physiotherapists, occupational therapists, dieticians, palliative care experts, and primary health care representatives. However, resource and time constraints were repeatedly described as barriers to include the patient perspective, leading some participants to question whether the involvement of additional health professionals was justified. In addition, ambivalence was expressed about sharing extensive patient perspective information because this would demand more resources and possibly limit the number of case discussions. Alternatively, more specialized MDTMs, for example, concerning rehabilitation or palliative care aspects, were suggested to facilitate more patient-centred approaches.

Added value of the patient perspective

Although the MDTM was described as a medical conference, several participants regarded the inclusion of the patient perspective as adding value to the discussion. References to patient perspective were broad, including social status and life situation, medical aspects such as physical status and comorbidity, and psychological and personal aspects such as patient preferences and views. According to the participants, such information was rarely included in MDTM discussions, and when it was, it was generally not decisive for the treatment recommendations. Some participants argued that the patient perspective was more important after the MDTM, as a foundation for how to care for the patient, than during the actual MDTM case discussions. One participant expressed this view as follows:

…the psychosocial, it doesn’t change the medical assessment at the MDTM, // but it does affect how we handle the case [i.e., the patient]. (Focus group interview no. 1)

The patient, together with the physician, was seen as responsible for deciding on treatment, and some participants stated that not taking the patient perspective into account would increase the risk that the final decision could deviate from MDTM recommendations. One participant explained this as follows:

…the best possible medical decision is probably made at the MDTM by consensus. // …then it might be something completely different, in the end anyway, when the patient is told what the recommendation is. Then you weigh everything up in the meeting with the patient. And that’s where the decision is really made. (Focus group interview no. 2)

This viewpoint had some participants arguing for the inclusion of patient perspectives in the MDTM case discussion to ensure that relevant treatment recommendations could be implemented.

Possibilities for patient contributions

Participants repeatedly stated that to include patient-related information in MDTM case discussions, patients needed to be able to contribute information relevant to them. Several participants claimed that most patients were informed that their case would be discussed at an MDTM in order to determine the best treatment option, but not all were alerted, which prevented them from contributing information. The importance of sharing information about treatment options and potential side effects in a way understandable to patients was described as a prerequisite for patient involvement. In this, the RN was seen as a mediator and interpreter of the information. According to some participants, patients often accepted MDTM treatment recommendations, but patients with low health literacy struggled to be involved in medical decisions. In such instances, patients should be able to rely on and trust health professionals to make correct judgments. As one participant stated it:

…I often hear “we recommend that we do this”. In this way we [i.e., health care professionals] put a lot of responsibility on the patients to make the decisions for themselves. And I also find that there are many patients who are not able to make decisions, who don’t want to make the decisions, who want us to take all the responsibility. (Focus group interview no. 3)

Some participants stated that patients and patient organizations demanded increased involvement in MDTMs. However, the general view was that the MDTM constitutes a professional forum in which patients, or patient representatives should not participate, since patient participation could introduce a risk of self-censorship and place the patient in an exposed situation. One participant stated:

I think it would be very frightening for the patient in many ways. Also, I think it puts a filter on it [i.e., the case discussions]. Self-censorship comes in, because you…you have to choose your words very carefully. (Focus group interview no. 2)

Patient involvement was seen in terms of the MDT considering the quality-of-life impacts of various treatment alternatives, and the patient getting the opportunity to contribute their perspective after the MDTM in discussing the recommendation.

Prerequisites for the patient perspective being considered in MDTM decision-making

Participants repeatedly stated that the RNs’ possibilities to participate and their roles in the MDTMs varied greatly. Furthermore, given the different ways there were to collect information on the patient perspective, several participants suggested using standardized formats to gather such information.

Structures promoting attention to the patient perspective

Some participants considered assumptions about the patient perspective a barrier to the successful implementation of MDTM recommendations. Repeated reference was made to the lack of structured ways to collect patient-related information prior to MDTMs. For example, some participants reported not having contact with the patient prior to the MDTM, which made it difficult to collect relevant patient-related information. In such cases, information was gathered from colleagues and clinical files/referral texts rather than from the patients themselves. Such information was, however, often considered second-hand information not fully qualified for consideration as conveying the patient perspective in MDTMs. To ensure that attention was paid to the patient perspective, standardized and structured assessments of the patient perspective were proposed as a way to ensure that patients are given the possibility to express their views and opinions. Such developments could increase the quality of the information and strengthen the focus on the patient perspective in MDTMs. In contrast, other participants questioned the value of such assessments prior to the MDTMs because of the uncertain outcomes of case discussions. Instead, more flexible ways of working were proposed, allowing them to participate when it was specifically relevant to them, for example, in complex case discussions. There was recognition that the responsibility for collecting and presenting the patient perspective in MDTMs needed to be clarified. Some participants suggested that the RN and the physician share this responsibility. An alternate view was that it was not important who presented the information, but rather that the information was indeed available and discussed. One participant reflected on this point as follows:

…why should you think that I as an RN am better at telling…what the patient ’s perspective is than the physician (?) There is nothing that says it is. // The most important thing is that it’s told. (Focus group interview no. 4)

Determinants of registered nurses’ contributions

RNs’ participation in and contributions to MDTMs were repeatedly described as hindered in three respects: barriers to attendance; perceived value of their information; and unclear role in the MDTM. Barriers to attendance consisted of RNs not always being considered mandatory participants in MDTMs and their participation being challenged by the timing of the MDTMs, which were planned around the physicians’ schedules. With MDTMs being time consuming and given the time allocated to their assignments, some participants said that they prioritized other work tasks over MDTM participation. Their contributions at the MDTMs was also hindered by the perceived value of their information. Some participants stated that their information on the patient perspective was generally not requested in MDTMs. This omission was concerning since the patient often shared relevant and personal information such as, for example, symptoms and worries with the RN. Several barriers to well-functioning teamwork in MDTMs were identified. One was hierarchical patterns that resulted in unbalanced participation with only a few active participants. Other factors were work experience, confidence, authority, discussion climate, and meeting structure. For some participants, contributing to the discussion was rather personal, requiring courage. One participant shared the following experience:

Sometimes you have it on the tip of your tongue; however, as you said, both of us are thick skinned…but sometimes the skin disappears. (Focus group interview no. 3)

Several participants described their roles in the MDTMs as unclear, with many disparate roles being reported, including coordinating investigations, planning, booking time slots, and contacting patients. Other participants primarily referred to a general educational aim. Despite ambiguous roles, the participants felt responsible for presenting the patient perspective at MDTMs and referred to themselves as patient advocates, spokespersons, or ambassadors. As an alternative to sharing the patient perspective in MDTMs, information was shared through documentation in medical records and during informal discussions with team colleagues. This experience was described by one participant as follows:

…I understand that the medical perspective is enormously important. But sometimes the nursing perspective is just as important. And it feels a bit sad that it doesn ’t come through. But we always find our own ways [to share information]//…you are the patient’s ambassador, in that way. (Focus group interview no. 2)

Regardless of their role in the MDTMs, several participants talked of the benefit of receiving important information at the meetings, which increased their knowledge about the patients. Afterwards, when meeting with the patients, the RNs could repeat and explain the treatment recommendations from a nursing perspective and make sure that the patients based their decisions on the right information, and thereby support the patients in making informed decisions.

Aiming to explore RNs’ views of prerequisites for and barriers to inclusion of the patient perspective in MDTMs in Swedish cancer care, this study shows that there is ambivalence regarding the aim of MDTMs in terms of whether the MDTM should have a medical or a more holistic focus. This study also shows that although articulating the patient perspective in MDTMs was described as valuable, it was generally not found to be decisive for the treatment recommendations. Moreover, the study describes barriers to RNs’ participation in MDTMs and their sharing of information on the patient perspective.

Several studies emphasize the importance of including information on the patient perspective in MDTMs [ 15 , 16 , 33 ]. However, despite the term “patient perspective” being frequently used, there are various views of what the concept really means in clinical practice. In this study we adopted a comprehensive approach to the concept, allowing the participants to share their understandings. This resulted in broad descriptions that included social information, comorbidities, and patient views. Lack of a clear understanding of the patient perspectives concepts in the MDTM context has also been reported in studies from the UK [ 34 ], pointing to the need for a clear definition. Although the patient perspective was recognized as valuable, our results demonstrates that the RN’s experience, the aim of the MDTM, and the question if and in such case when the patient perspective should be considered is not fully explored. Since the MDTM, as a collective discussion and decision forum, aims to provide the best possible treatment recommendations for individual patients, the alignment of expectations in this area is needed to allow team members to agree on relevant information.

Lamb et al. [ 9 ] reported that patients should be represented by the MDTM participant who knows them best, which is concordant with this study showing that it is not important which profession presents the patient perspective, but does still suggest a shared responsibility between RNs and physicians. Bate et al. [ 35 ] also describe patients wanting their perspective to be represented by someone who has met them, but also often describing the RN as best suited for this task. According to other studies, this preference is partly because RNs have more continuity of patient contact throughout the clinical trajectory [ 5 , 36 ], a finding in agreement with this study, in which the RNs described themselves as responsible to act on behalf of patients’ interests and to share information on the patient perspective. At the same time participants in our study describe their role during the MDTM as unclear, with the RN not always considered a mandatory team member. Participants often questioned the value of their contributions, since the information they could share was not asked for, an experience that possibly fed into the sense of an unclear role. Our results strengthen earlier studies that have reported limited or no contributions from non-medical disciplines in MDTMs [ 5 , 16 , 37 ], referring specifically to participants such as RNs. This lack of contribution can partly be explained by disease-focused case discussions and by MDTM discussions primarily driven by physicians [ 34 ]. It may also relate to the lack of defined roles in MDTMs in Swedish national guidelines. In addition, personal and team-related factors such as hierarchy, training in non-technical skills, work experience, confidence, and discussion climate all play roles, with an open climate as opposed to tension among participants possibly influencing RN contributions to case discussions [ 16 , 38 ].

Our findings similarly suggest that participation in MDTMs was rather personal, requiring courage from the participants to contribute. Stewart et al. [ 39 ] reported that 19% of cancer nurse specialists (CNSs) who participated in MDTMs found the activity uncomfortable or intimidating, and only half of the respondents said that they would challenge MDTM participants. Other studies demonstrated that CNSs found alternative ways to contribute to MDTMs by sharing information, asking questions, using humour, providing practical alternatives, or by framing contributions in medical terms; these approaches affected the MDTMs in different ways by facilitating discussion, the decision-making process, and teamwork [ 8 , 36 ]. An interesting ambivalence emerged in our study regarding participants reporting unclear roles in and limited contributions to MDTMs, while describing their responsibility to act on behalf of patients’ interests. This result leaves us with the following question: Do the RNs take a passive and subordinated position in the MDTM or do they value their contribution as insignificant? Simone Roach’s framework of caring, “six Cs”, include the attribute commitment [in caring], described as a complex affective response that influences the convergence between desires and obligations, including accepting limitations and prioritizing tasks [ 40 ]. This idea was reflected in this study by participants describing a high caseload at MDTMs and suggesting participating selectively in the cases relevant to them. This could potentially enhance commitment and contributions to the cases selected. Wallace et al. [ 8 ] supported this idea, reporting that RNs’ contributions to MDTMs were particularly relevant in complex and relapsed cases and when patient preferences, psychosocial factors, or communication difficulties could affect treatment decisions. Yet, this idea raises questions regarding who decides on the case selection criteria, when it is relevant to include patient-related information, and when RNs should participate. To support active contributions to MDTMs and an enhanced focus on the patient perspective, clarification of the national mission statement for CNs, covering the role description, and compulsory CN specialist course to support RNs’ active engagement in strengthening their role in MDTMs may be necessary. Work experience is suggested to impact on the RNs possibility to contribute to MDTMs [ 41 ] but whether RNs’ education level affect their contributions to the MDTMs is unknown and merits further investigation.

The participants’ ambivalence regarding the medical versus holistic perspectives and descriptions of an unclear role in MDTMs can be seen as symptoms of organizational ambiguity. If MDTMs are to be patient-centered, holistic information should be integrated in the discussion and cover medical factors as well as patient-related perspectives [ 8 ]. The results indicate that the patient perspective should preferably be shared by the patients themselves and not be collected from, for example, clinical files and referral texts, since these were considered insufficient. However, this study found that the opportunities to collect this information were hindered by a lack of structured assessment, unclear division of responsibilities within the MDT, and the fact that some RNs did not have contact with the patients before the MDTMs. In this study, participants proposed the use of structured assessments of the patient perspective to increase the value of the information but also to standardize information management. Taylor et al. [ 34 ] suggested that such developments would strengthen the RN’s role as an MDTM participant responsible for holistic needs. Introducing of such tools would require that the MDT define relevant patient-related aspects to be considered, since key information may vary between diagnoses and in relation to the treatment options discussed. Some participants in this study described a need to include other allied health professionals to uphold a holistic approach to the MDTM discussion, an approach that needs to be balanced against the effective use of resources. To address patients’ complex needs, Horlait et al. [ 5 ] argue for a culture change that enhances interdisciplinary teamwork. To achieve holistic discussions and individualized treatment recommendations, MDTs may need to develop interdisciplinarity rather than multidisciplinarity and integrate knowledge from several relevant sources and perspectives [ 5 , 42 ]. This development would call for the clarification of roles and responsibilities, identifying factors that influence MDTM performance, team education, and structured team-based evaluations. As an alternative to extending the MDT to ensure a more holistic approach to the MDTM, some participants in our study suggested that the patient perspective rather be considered after the MDTM, potentially through establishing a separate forum to discuss the treatment recommendations from the patient’s perspective. This proposal is in line with a study by Winters et al. [ 43 ], which discussed alternative ways to integrate the patient perspective in MDTMs using “post-MDTM consultations” in which the biomedically based MDTM recommendations are adapted to the patients’ abilities, circumstances, and wishes.

To decrease potential organizational ambiguity, the aim of the MDTM may need to be more precisely defined, including a clearer description of what information should be discussed and the different participants’ roles and responsibilities. To determine whether the patient perspective should be included in MDTMs and how, the question of what benefits patients the most must be answered: Should their perspective be included in the MDTM discussion, or should the MDT conduct a medically focused discussion before considering the patient perspective [ 9 ]? In previous research, MDT members identified the specialist nurse as having an important role as the central point of contact for the patient, offering support throughout the clinical trajectory and advocating for the patient in the MDTM [ 44 ]. This is aligned with our results, in which RNs described themselves as responsible for presenting the patient perspective. Yet, in contrast, some participants stated that the MDTM case discussion should be medically focused and that RNs should only participate in selected case discussions. This challenges a patient-centered care approach and underlines the importance of clarifying the RN’s role and responsibilities within the MDTM.

Strengths and limitations

This study explores a subject that has received limited attention in earlier research, namely, RNs’ views of the patient perspective in MDTMs. It provides a qualitative perspective and in-depth insights. Most of the included RNs participated regularly in MDTMs; a small number of RNs did not participate but were nevertheless well aware of MDTM procedures, bringing valuable information to the focus group interviews as they met the patients before and after MDTMs. A strength of this study is that the authors have various backgrounds and experiences in cancer care, with clinical, academic, and organisational experience ensuring a variety of perspectives. One author participated in a focus group interview before being recruited to the research group, which might be seen as introducing a potential bias [ 45 , 46 ]. However, transparent and continuous discussions between the authors included self-reflection and self-scrutiny to ensure valid and grounded interpretations of the data [ 46 ]. Reliability was further strengthened by discussions leading to joint agreements on key themes and subthemes [ 27 ]. Credibility was strengthened by recurrent confirmation of the analysis with references to the transcripts and notes. The results are supported by representative quotations, increasing the conformability. A limitation of the study is that the participants reflected on their own experiences in the context of Swedish cancer care, making transferability less certain. Sample size is crucial for credibility [ 47 ] and our study is based on four focus group interviews with representation from different diagnostic areas, clinics, and hospital types. This is considered a strength since it enhance variation in the participating RNs’ knowledge of the research subject based on the varying roles of university and county hospitals, and on the fact that the RN role varies between diagnostic groups due to the complexity of the cancer diagnosis, the health care organization and the prerequisites for their assignments. Although we cannot exclude the possibility that including additional RNs could have brought additional perspectives, data saturation was nevertheless perceived to have been attained during the content analysis [ 47 ]. Furthermore, the inclusion of various hospitals and diagnostic areas may have blurred roles and responsibilities for the patient perspective in certain MDTs.

This study reveals various views of information concerning consideration of the patient perspective in MDTMs and the need to clarify the RN’s role in these meetings. There was uncertainty surrounding the overall impact of the patient perspective, indicating an organizational ambiguity regarding MDTMs. As the patients do not participate in MDTMs, our results show that, if enhanced presentation of patient perspective in MDTMs is desired, key information points and structures must be established to collect and present relevant patient-related information. Also, further research is needed on how and when RNs should optimally be integrated throughout the MDTM decision-making process.

Availability of data and materials

The datasets generated and/or analysed during the current study cannot be shared publicly due to regulations in the Swedish Data Protection Act (2018:218; 2019:219) and Ethical Review Act (2003:460), but data are available from the corresponding author on reasonable request.

Abbreviations

Contact nurse

Cancer nurse specialists

Multidisciplinary team

Multidisciplinary team meeting

Registered nurse

Holmes A, Kelly BD, Perera M, Eapen RS, Bolton DM, Lawrentschuk N. A systematic scoping review of multidisciplinary cancer team and decision-making in the management of men with advanced prostate cancer. World J Urol. 2021;39(2):297–306.

Article   CAS   Google Scholar  

Edney LC, Gray J, Karnon J. A scoping review of the economics of multidisciplinary teams in oncology care. J Cancer Policy. 2020;26:100257.

Article   Google Scholar  

Specchia ML, Frisicale EM, Carini E, Di Pilla A, Cappa D, Barbara A, et al. The impact of tumor board on cancer care: evidence from an umbrella review. BMC Health Serv Res. 2020;20(1):73.

Soukup T, Lamb BW, Arora S, Darzi A, Sevdalis N, Green JS. Successful strategies in implementing a multidisciplinary team working in the care of patients with cancer: an overview and synthesis of the available literature. J Multidiscip Healthc. 2018;11:49–61.

Horlait M, Baes S, De Regge M, Leys M. Understanding the complexity, underlying processes, and influencing factors for optimal multidisciplinary teamwork in hospital-based cancer teams: a systematic integrative review. Cancer Nurs. 2021;44(6):E476–e92.

Gandamihardja TAK, Soukup T, McInerney S, Green JSA, Sevdalis N. Analysing breast cancer multidisciplinary patient management: a prospective observational evaluation of team clinical decision-making. World J Surg. 2019;43(2):559–66.

Rosell L, Wihl J, Hagberg O, Ohlsson B, Nilbert M. Function, information, and contributions: an evaluation of national multidisciplinary team meetings for rare cancers. Rare Tumors. 2019;11:2036361319841696.

Wallace I, Barratt H, Harvey S, Raine R. The impact of clinical nurse specialists on the decision making process in cancer multidisciplinary team meetings: a qualitative study. Eur J Oncol Nurs. 2019;43:101674.

Lamb BW, Taylor C, Lamb JN, Strickland SL, Vincent C, Green JS, et al. Facilitators and barriers to teamworking and patient centeredness in multidisciplinary cancer teams: findings of a national study. Ann Surg Oncol. 2013;20(5):1408–16.

Restivo L, Apostolidis T, Bouhnik AD, Garciaz S, Aurran T, Julian-Reynier C. Patients' non-medical characteristics contribute to collective medical decision-making at multidisciplinary oncological team meetings. PLoS One. 2016;11(5):e0154969.

Wihl J, Rosell L, Carlsson T, Kinhult S, Lindell G, Nilbert M. Medical and nonmedical information during multidisciplinary team meetings in cancer care. Curr Oncol (Toronto, Ont). 2021;28(1):1008–16.

Rosell L, Wihl J, Nilbert M, Malmström M. Health Professionals' views on key enabling factors and barriers of National Multidisciplinary Team Meetings in cancer care: a qualitative study. J Multidiscip Healthc. 2020;13:179–86.

Hahlweg P, Didi S, Kriston L, Harter M, Nestoriuc Y, Scholl I. Process quality of decision-making in multidisciplinary cancer team meetings: a structured observational study. BMC Cancer. 2017;17(1):772.

Geerts PAF, van der Weijden T, Savelberg W, Altan M, Chisari G, Launert DR, et al. The next step toward patient-centeredness in multidisciplinary cancer team meetings: an interview study with professionals. J Multidiscip Healthc. 2021;14:1311–24.

Hamilton DW, Heaven B, Thomson RG, Wilson JA, Exley C. Multidisciplinary team decision-making in cancer and the absent patient: a qualitative study. BMJ Open. 2016;6(7):e012559.

Hahlweg P, Hoffmann J, Härter M, Frosch DL, Elwyn G, Scholl I. In absentia: an exploratory study of how patients are considered in multidisciplinary cancer team meetings. PLoS One. 2015;10(10):e0139921.

Heuser C, Diekmann A, Schellenberger B, Bohmeier B, Kuhn W, Karbach U, et al. Patient participation in multidisciplinary tumor conferences from the Providers' perspective: is it feasible in routine cancer care? J Multidiscip Healthc. 2020;13:1729–39.

Soukup T, Sevdalis N, Green JSA, Lamb BW, Chapman C, Skolarus TA. Making tumor boards more patient-centered: Let's start with the name. JCO Oncol Pract. 2021;17(10):591–3.

Lamb BW, Jalil RT, Sevdalis N, Vincent C, Green JS. Strategies to improve the efficiency and utility of multidisciplinary team meetings in urology cancer care: a survey study. BMC Health Serv Res. 2014;14:377.

Devitt B, Philip J, Singh M, McLachlan SA. Understanding Patients' attitudes toward cancer multidisciplinary meetings: a mixed methods study. JCO Oncol Pract. 2020;16(2):e175–e82.

Diekmann A, Heuser C, Ernstmann N, Geiser F, Groß SE, Midding E, et al. How do breast cancer patients experience multidisciplinary tumor conferences? - a description from the patient perspective. Breast. 2019;44:66–72.

Jeyathevan G, Lemonde M, Brathwaite AC. The role of oncology nurse navigators in facilitating continuity of care within the diagnostic phase for adult patients with lung cancer. Canadian oncology nursing journal = Revue canadienne de nursing oncologique. 2017;27(1):74–80.

Lamb BW, Jalil RT, Shah S, Brown K, Allchorne P, Vincent C, et al. Cancer patients' perspectives on multidisciplinary team working: an exploratory focus group study. Urol Nurs. 2014;34(2):83–91, 102.

Rowlands S, Callen J. A qualitative analysis of communication between members of a hospital-based multidisciplinary lung cancer team. Eur J Cancer Care. 2013;22(1):20–31.

Alexandersson N, Rosell L, Wihl J, Ohlsson B, Steen Carlsson K, Nilbert M. Determinants of variable resource use for multidisciplinary team meetings in cancer care. Acta oncologica (Stockholm, Sweden). 2018;57(5):675–80.

Krueger RA, Casey MA. Focus groups: a practical guide for applied reseach. 5th ed. Thousand Oaks: Sage Publications; 2015.

Google Scholar  

Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs. 2008;62(1):107–15.

O'Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med. 2014;89(9):1245–51.

Wright FC, De Vito C, Langer B, Hunter A. Multidisciplinary cancer conferences: a systematic review and development of practice standards. Eur J Cancer (Oxford, England : 1990). 2007;43(6):1002–10.

Wihl J, Rosell L, Bendahl PO, De Mattos CBR, Kinhult S, Lindell G, et al. Leadership perspectives in multidisciplinary team meetings; observational assessment based on the ATLAS instrument in cancer care. Cancer Treatment Res Commun. 2020;25:100231.

Statens offentliga utredningar. En nationell cancerstrategi för framtiden [ a national cancer strategy for the future ] (SOU 2009:11). Stockholm: Fritzes Offentliga Publikationer; 2009.

Regionala cancercentrum i samverkan. Nationell beskrivning av kontaktsjuksköterskans uppdrag [ national description of the contact nurse assignment ]. Stockholm: Regionala cancercentrum i samverkan; 2019. Available from: https://cancercentrum.se/globalassets/patient-och-narstaende/samverkan/kssk/nationellt-uppdrag-for-kontaktsjukskoterska.pdf

Nazim SM, Fawzy M, Bach C, Ather MH. Multi-disciplinary and shared decision-making approach in the management of organ-confined prostate cancer. Arab J Urol. 2018;16(4):367–77.

Taylor C, Finnegan-John J, Green JS. "no decision about me without me" in the context of cancer multidisciplinary team meetings: a qualitative interview study. BMC Health Serv Res. 2014;14:488.

Bate J, Wingrove J, Donkin A, Taylor R, Whelan J. Patient perspectives on a national multidisciplinary team meeting for a rare cancer. Eur J Cancer Care. 2019;28(2):e12971.

Tod AM, Redman J, McDonnell A, Borthwick D, White J. Lung cancer treatment rates and the role of the lung cancer nurse specialist: a qualitative study. BMJ Open. 2015;5(12):e008587.

Rosell L, Alexandersson N, Hagberg O, Nilbert M. Benefits, barriers and opinions on multidisciplinary team meetings: a survey in Swedish cancer care. BMC Health Serv Res. 2018;18(1):249.

Rankin NM, Lai M, Miller D, Beale P, Spigelman A, Prest G, et al. Cancer multidisciplinary team meetings in practice: results from a multi-institutional quantitative survey and implications for policy change. Asia-Pacific J Clin Oncol. 2018;14(1):74–83.

Stewart I, Leary A, Tod A, Borthwick D, Khakwani A, Hubbard R, et al. Barriers to delivering advanced cancer nursing: a workload analysis of specialist nurse practice linked to the English National Lung Cancer Audit. Eur J Oncol Nurs. 2018;36:103–11.

Roach MS. Caring, the human mode of being: a blueprint for the health professions. Ottawa: Canadian Healthcare Association Press; 2002. (2nd rev. ed.). Copy from Archives of Caring in Nursing, Christine E. Lynn College of Nursing, Florida Atlantic University, ARC-005 Sister M. Simone Roach Papers, 1958–2005, used by premission.

Horlait M, De Regge M, Baes S, Eeckloo K, Leys M. Exploring non-physician care professionals' roles in cancer multidisciplinary team meetings: a qualitative study. PLoS One. 2022;17(2):e0263611.

Choi BC, Pak AW. Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services, education and policy: 1. Definitions, objectives, and evidence of effectiveness. Clin Invest Med. 2006;29(6):351–64.

Winters DA, Soukup T, Sevdalis N, Green JSA, Lamb BW. The cancer multidisciplinary team meeting: in need of change? History, challenges and future perspectives. BJU Int. 2021;128(3):271–9.

Cook O, McIntyre M, Recoche K, Lee S. "our nurse is the glue for our team" - multidisciplinary team members' experiences and perceptions of the gynaecological oncology specialist nurse role. Eur J Oncol Nurs. 2019;41:7–15.

Johnston CM, Wallis M, Oprescu FI, Gray M. Methodological considerations related to nurse researchers using their own experience of a phenomenon within phenomenology. J Adv Nurs. 2017;73(3):574–84.

Whittemore R, Chase SK, Mandle CL. Validity in qualitative research. Qual Health Res. 2001;11(4):522–37.

Elo S, Kääriäinen M, Kanste O, Pölkki T, Utriainen K, Kyngäs H. Qualitative content analysis: a focus on trustworthiness. SAGE Open. 2014;4(1):2158244014522633.

Download references

Acknowledgements

We would like to thank all the participants who contributed to this study by providing valuable information.

Open access funding provided by Lund University. No funding was received for this study.

Author information

Authors and affiliations.

Regional Cancer Centre South, Region Skåne, Lund, Sweden

Linn Rosell & Wenche Melander

Division of Oncology, Department of Clinical Sciences Lund, Lund University, Scheeletorget 1, 22 363, Lund, Sweden

Linn Rosell & Mef Nilbert

Department of Health Sciences, Faculty of Medicine, Lund University, Lund, Sweden

Wenche Melander, Berit Lindahl & Marlene Malmström

Department of Surgery and Gastroenterology, Skåne University Hospital, Lund, Sweden

Wenche Melander & Marlene Malmström

Institute for Palliative Care, Lund University and Region Skåne, Lund, Sweden

Berit Lindahl

You can also search for this author in PubMed   Google Scholar

Contributions

All authors have participated in relevant steps in the research process, contributed to the manuscript and given the final approval for publication. Concept and design: MN, MM, LR. Data collection: MM, LR. Data analysis and interpretation: All authors. Manuscript writing: All authors. Final approval of manuscript: All authors.

Corresponding author

Correspondence to Linn Rosell .

Ethics declarations

Ethics approval and consent to participate.

The study was performed in accordance with the principles of the Helsinki Declaration. The study was approved by the Regional Ethics Review Board in Lund, Sweden (registration number 2016/195). The participants received both oral and written information about the study and informed written consent was collected from all participants prior to the interviews. The participants’ confidentiality was secured by reporting findings on a group level. No personally identifiable information is included in the analysis.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Rosell, L., Melander, W., Lindahl, B. et al. Registered nurses’ views on consideration of patient perspectives during multidisciplinary team meetings in cancer care. BMC Nurs 21 , 350 (2022). https://doi.org/10.1186/s12912-022-01127-2

Download citation

Received : 22 April 2022

Accepted : 17 October 2022

Published : 09 December 2022

DOI : https://doi.org/10.1186/s12912-022-01127-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Nurse navigator
• Patient perspective
• Inter-disciplinary team
• Tumour board
• Cancer care

BMC Nursing

ISSN: 1472-6955

• General enquiries: [email protected]

• Introduction
• Conclusions
• Article Information

Point estimates reflect participants’ willingness to share for a subset of scenarios from the conjoint experiment (willingness to share evaluated on a 1-5 scale). Each scenario had approximately 591 participants randomized to that scenario (the number of respondents ranged from 569 to 606). Panel A represents a university hospital and Panel B represents a digital technology company as the user of the data.

Appendix 1. Conjoint Survey Instrument

Appendix 2. Conjoint Design Table

See More About

Sign up for emails based on your interests, select your interests.

Customize your JAMA Network experience by selecting one or more topics from the list below.

• Academic Medicine
• Acid Base, Electrolytes, Fluids
• Allergy and Clinical Immunology
• American Indian or Alaska Natives
• Anesthesiology
• Anticoagulation
• Art and Images in Psychiatry
• Artificial Intelligence
• Assisted Reproduction
• Bleeding and Transfusion
• Caring for the Critically Ill Patient
• Challenges in Clinical Electrocardiography
• Climate and Health
• Climate Change
• Clinical Challenge
• Clinical Decision Support
• Clinical Implications of Basic Neuroscience
• Clinical Pharmacy and Pharmacology
• Complementary and Alternative Medicine
• Consensus Statements
• Coronavirus (COVID-19)
• Critical Care Medicine
• Cultural Competency
• Dental Medicine
• Dermatology
• Diabetes and Endocrinology
• Diagnostic Test Interpretation
• Drug Development
• Electronic Health Records
• Emergency Medicine
• End of Life, Hospice, Palliative Care
• Environmental Health
• Equity, Diversity, and Inclusion
• Facial Plastic Surgery
• Gastroenterology and Hepatology
• Genetics and Genomics
• Genomics and Precision Health
• Global Health
• Guide to Statistics and Methods
• Hair Disorders
• Health Care Delivery Models
• Health Care Economics, Insurance, Payment
• Health Care Quality
• Health Care Reform
• Health Care Safety
• Health Care Workforce
• Health Disparities
• Health Inequities
• Health Policy
• Health Systems Science
• History of Medicine
• Hypertension
• Images in Neurology
• Implementation Science
• Infectious Diseases
• Innovations in Health Care Delivery
• JAMA Infographic
• Law and Medicine
• Leading Change
• Less is More
• LGBTQIA Medicine
• Lifestyle Behaviors
• Medical Coding
• Medical Devices and Equipment
• Medical Education
• Medical Education and Training
• Medical Journals and Publishing
• Mobile Health and Telemedicine
• Narrative Medicine
• Neuroscience and Psychiatry
• Notable Notes
• Nutrition, Obesity, Exercise
• Obstetrics and Gynecology
• Occupational Health
• Ophthalmology
• Orthopedics
• Otolaryngology
• Pain Medicine
• Palliative Care
• Pathology and Laboratory Medicine
• Patient Care
• Patient Information
• Performance Improvement
• Performance Measures
• Perioperative Care and Consultation
• Pharmacoeconomics
• Pharmacoepidemiology
• Pharmacogenetics
• Pharmacy and Clinical Pharmacology
• Physical Medicine and Rehabilitation
• Physical Therapy
• Physician Leadership
• Population Health
• Primary Care
• Professional Well-being
• Professionalism
• Psychiatry and Behavioral Health
• Public Health
• Pulmonary Medicine
• Regulatory Agencies
• Reproductive Health
• Research, Methods, Statistics
• Resuscitation
• Rheumatology
• Risk Management
• Scientific Discovery and the Future of Medicine
• Shared Decision Making and Communication
• Sleep Medicine
• Sports Medicine
• Stem Cell Transplantation
• Substance Use and Addiction Medicine
• Surgical Innovation
• Surgical Pearls
• Teachable Moment
• Technology and Finance
• The Art of JAMA
• The Arts and Medicine
• The Rational Clinical Examination
• Tobacco and e-Cigarettes
• Translational Medicine
• Trauma and Injury
• Treatment Adherence
• Ultrasonography
• Users' Guide to the Medical Literature
• Vaccination
• Venous Thromboembolism
• Veterans Health
• Women's Health
• Workflow and Process
• Wound Care, Infection, Healing

Get the latest research based on your areas of interest.

Others also liked.

• Download PDF
• X Facebook More LinkedIn

Grande D , Mitra N , Iyengar R, et al. Consumer Willingness to Share Personal Digital Information for Health-Related Uses. JAMA Netw Open. 2022;5(1):e2144787. doi:10.1001/jamanetworkopen.2021.44787

Manage citations:

© 2024

• Permissions

Consumer Willingness to Share Personal Digital Information for Health-Related Uses

• 1 Division of General Internal Medicine, University of Pennsylvania, Philadelphia
• 2 Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
• 3 Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia
• 4 Wharton School, University of Pennsylvania, Philadelphia
• 5 Department of Emergency Medicine, University of Pennsylvania, Philadelphia
• 6 Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia
• 7 Department of Family Medicine and Community Health, University of Pennsylvania, Philadelphia

Question   What factors are associated with consumers’ willingness to share their digital information for health-related uses?

Findings   In this survey study of 3543 US adults, consumer willingness to share digital data was associated with a range of factors, most importantly the source and type of data. Certain data (eg, financial, social media, public cameras) were viewed as more sensitive than electronic health record data, but underlying views on digital health privacy were strongly associated with consumer views on sharing any digital information.

Meaning   In this study, many consumers were reluctant to share their digital data for health-related uses, suggesting that new privacy protections may be needed to increase consumer trust.

Importance   Consumers routinely generate digital information that reflects on their health.

Objective   To evaluate the factors associated with consumers’ willingness to share their digital health information for research, health care, and commercial uses.

Design, Setting, and Participants   This national survey with an embedded conjoint experiment recruited US adults from a nationally representative sample, with oversampling of Black and Hispanic panel members. Participants were randomized to 15 scenarios reflecting use cases for consumer digital information from a total of 324 scenarios. Attributes of the conjoint analysis included 3 uses, 3 users, 9 sources of digital information, and 4 relevant health conditions. The survey was conducted from July 10 to 31, 2020.

Main Outcomes and Measures   Participants rated each conjoint profile on a 5-point Likert scale (1-5) measuring their willingness to share their personal digital information (with 5 indicating the most willingness to share). Results reflect mean differences in this scale from a multivariable regression model.

Results   Among 6284 potential participants, 3543 (56%) responded. A total of 1862 participants (53%) were female, 759 (21%) identified as Black, 834 (24%) identified as Hispanic, and 1274 (36%) were 60 years or older. In comparison with information from electronic health care records, participants were less willing to share information about their finances (coefficient, −0.56; 95% CI, −0.62 to −0.50), places they visit from public cameras (coefficient, −0.28; 95% CI, −0.33 to −0.22), communication on social media (coefficient, −0.20; 95% CI −0.26 to −0.15), and their search history from internet search engines (coefficient, −0.11; 95% CI, −0.17 to −0.06). They were more willing to share information about their steps from applications on their phone (coefficient, 0.22; 95% CI, 0.17-0.28). Among the conjoint attributes, the source of information (importance weight: 59.1%) was more important than the user (17.3%), use (12.3%), and health condition (11.3%). Four clusters of consumers emerged from the sample with divergent privacy views. While the context of use was important, these 4 groups expressed differences in their overall willingness to share, with 337 participants classified as never share; 1116 classified as averse to sharing (mean rating, 1.64; 95% CI, 1.62-1.65); 1616 classified as uncertain about sharing (mean rating, 2.84; 95% CI, 2.81-2.86); and 474 classified as agreeable to sharing (mean rating, 4.18; 95% CI, 4.16-4.21). Respondents who identified as White and non-Hispanic, had higher income, and were politically conservative were more likely to be in a cluster that was less willing to share (ie, never or averse clusters).

Conclusions and Relevance   These findings suggest that although consumers’ willingness to share personal digital information for health purposes is associated with the context of use, many have strong underlying privacy views that affect their willingness to share. New protections may be needed to give consumers confidence to be comfortable sharing their personal information.

Twenty-five years ago, the Health Insurance Portability and Accountability Act (HIPAA) of 1996 was signed into law. 1 The privacy provisions were intended to strengthen protections over health information generated in the course of health care delivery. 2 Since that time, consumer digital information created outside of health care encounters has proliferated, with much of this information reflecting personal health. The proliferation of consumer digital data alongside modern data science and an understanding of health’s social determinants has effaced any lines between health and nonhealth information such that most digital data are now health data. 3 The result is that the health data generated in the context of clinical care is substantially protected, but information potentially as health-revealing that is collected in other contexts is not.

Although prior studies have demonstrated that consumers care deeply about health privacy, 4 - 6 views on privacy differ widely based on contextual factors, such as the perceived social benefits of the use, the motive of the user (including if the use is for commercial gain), and the sensitivity of the information itself. 7 - 9 Digital data has many potential health-related applications. Social media chatter has been mined to identify individuals with mental health concerns that can be directly addressed through outreach. 10 - 12 Other technologies have been used to suggest improvements to sleep habits, encourage physical activity, and detect falls in the home. 13 - 16 Digital data have also powered responses to the COVID-19 pandemic; mobile devices have been used to track high-risk COVID-19 exposures while also contributing data on risk factors for disease transmission. 10 , 11 Alongside these new applications, privacy concerns have grown. A recent analysis found that only 32% of COVID-19 applications available explicitly stated that user data will be anonymized, encrypted, and secured. 17 There are additional concerns that consumers often cannot turn off the collection of personal data—companies, including search engines and social media sites, can deduce locations of individuals even if users have opted out of sharing location data. 18 , 19

These tensions between individual loss of privacy and potential benefits lead to important questions about what determines acceptable use for consumers of their data. We studied a nationally representative population to determine what factors are associated with greater or lesser willingness to share personal digital data related to health.

Participants were recruited for this cross-sectional survey study from the web-enabled Ipsos KnowledgePanel. Details regarding participant sampling, recruitment, and survey administration can be found in a prior study published from this survey. 20 In summary, Ipsos is a probability-based panel that is designed to be representative of the US population, in which participants were recruited using address-based sampling methods. 21 At the time of recruitment, participants were asked to complete a general informed consent process followed by a core survey profile in which participants self-reported key demographic characteristics including race and ethnicity using the US Census Bureau categories. Black and Hispanic panel members were oversampled for this study to permit subgroup analyses. Race and ethnicity were assessed in this study, as prior evidence demonstrates that in certain circumstances, members of racial and ethnic minority groups are more concerned about their privacy. 22 , 23

The survey was administrated between July 10 and July 31, 2020, in Spanish and English. All data received by the study team were deidentified. This study was reviewed and determined exempt by the University of Pennsylvania institutional review board. This study followed the ethical conduct and reporting guidelines of public opinion and survey research defined by the American Association for Public Opinion Research ( AAPOR ).

This study was designed to use conjoint analysis to measure consumer preferences with respect to sharing digital health information. Conjoint analysis has been widely used in marketing and increasingly in health-related applications to measure preferences that more closely reflect revealed interests that may be repeated in other contexts and decisions. 24 - 26 Conjoint analysis also allows the examination of a large number of product, program, or policy attributes. We evaluated 4 digital information use attributes in the scenarios: the information being used (information type), who is using it (user), the purpose of use (use), and the health condition reflected in the use (health condition). The experimental design included 324 possible scenarios reflecting a full factorial design of 9 information types, 3 users, 3 uses, and 4 health conditions (9 × 3 × 3 × 4 = 324). The survey instrument was adapted from a prior instrument using conjoint analysis to assess consumer privacy preferences related to reusing information obtained from electronic health records (EHRs). 27 The conjoint attributes and levels were selected based on qualitative interviews with consumers and subject matter experts. 3 , 28 We conducted interviews 29 to evaluate the survey instrument for clarity and participant comprehension prior to administration.

Participants were presented with a brief introduction to the topic of digital health data reuse (eAppendix 1 in the Supplement ). The introduction described that there are many sources of digital health information, this information has a broad range of applications, and that it is often possible to identify individuals from the data they leave behind. They were then asked to evaluate 15 scenarios (ie, profiles) randomly selected from the 324 total. Participants rated each scenario on a 5-point scale assessing their willingness to share their information: 1 represented definitely would share, and 5 represented definitely would not share. We reversed the scale in analyses for interpretability of results.

Scenarios were constructed (eAppendix 2 in the Supplement ) using 3 different users of the participant’s data: a university hospital, a pharmaceutical company, and a digital technology company. The 3 possible uses of data included research, health care quality improvement, and marketing. The 4 health conditions included cancer, diabetes, depression, and COVID-19. COVID-19 was added because of its relevance during the time of the survey, and we hypothesized participants might be motivated to share data to control its spread.

The information types were chosen to reflect a range relevant for health, including personal spending and finances through banks and credit cards, places visited via public cameras, communication via social media, internet searches via search engines, places visited via smartphone applications, health via EHRs, purchases via online retail, genetic information via consumer genetic testing companies, and walking via smartphone applications.

Panel recruitment rates were calculated by Ipsos KnowledgePanel and are reported elsewhere. 20 Study-specific completion rates (percentage of those invited to participate that responded) are reported in the Results section as recommended for online probability samples. 30

In conjoint analysis, a profile is described in terms of attributes, with each attribute taking specific values. The overall attractiveness of a profile is based on how much each of these parts is worth to the decision-maker (ie, the part-worth utilities). In this study, the part-worth utilities for each level of each conjoint attribute were computed using a generalized estimating equation (GEE) model under a Gaussian distribution and identity link and assuming an independent working correlation structure with robust, empirical SEs. In these models, positive coefficients represent more favored levels and negative coefficients represent less favored levels as compared with a baseline level for each attribute. For each attribute, the range of the part-worths (ie, maximum minus minimum) provides a signal of how important that attribute is in determining the attractiveness of the profiles. To facilitate a comparison of the importance across attributes, the range of each attribute is normalized by the sum of the ranges across attributes. These percentage importance weights were calculated for each attribute. Poststratification weights provided by Ipsos were used in all analyses; these weights ensure that the sample is representative of the US population (by comparing the sample with population benchmarks from the Current Population Survey), reflect study-specific design (eg, oversampling of certain populations), and account for differential distributions of participant and nonparticipant characteristics. All hypothesis tests were 2-sided; P  < .05 was considered statistically significant.

In addition, a latent class analysis was used to identify and describe clusters of participants who responded similarly to conjoint profiles. The final model (3 clusters) was selected based on Akaike information criterion (AIC) and the Bayesian information criterion (BIC) in conjunction with pragmatic interpretation of results. We assigned 337 participants to their own cluster prior to running the latent class analysis because there was no variability in their responses to all conjoint scenarios (ie, they answered definitely would not share to all). Latent class analysis was conducted in R version 4.0.5 using the flexmix package (R Project for Statistical Computing). All other analyses were conducted in Stata version 16 (StataCorp). Descriptive statistics for the 3 clusters and the excluded group were calculated and compared using χ 2 statistics.

We surveyed 6284 potential participants; 3543 responded (56%). Table 1 describes the characteristics of the survey respondents. A total of 1862 participants (53%) were female, 759 (21%) identified as Black, 834 (24%) identified as Hispanic, and 1274 (36%) were 60 years or older.

Results from the conjoint experiment using a homogenous model are summarized in Table 2 . The relative importance (importance weight on a 0%-100% scale) was greatest for information type (59.1%) followed by user (17.3%), use (12.3%), and disease (11.3%).

Model coefficients in Table 2 represent differences in the primary outcome measure, ie, willingness to share personal digital information (range, 1-5, with 1 indicating definitely would not share and 5 indicating definitely would share). In comparison with health information from personal health care records (ie, EHRs), participants were less willing to share information about their finances from financial institutions (coefficient, −0.56; 95% CI, −0.62 to −0.50), places they visit from public cameras (coefficient, −0.28; 95% CI, −0.33 to −0.22), communication with other people on social media (coefficient, −0.20; 95% CI, −0.26 to −0.15), and their search history from internet search engines (coefficient, −0.11; 95% CI, −0.17 to −0.06). Participants were more willing to share information about their walking activity from applications on their phone (coefficient, 0.22; 95% CI, 0.17-0.28) in comparison with information from their EHR. There were no differences in participants’ willingness to share their genetic information from consumer genetic testing companies, retail purchase history from online retail stores (past purchases), or location information about places they visit from their mobile phone compared with their personal EHR data.

Compared with a university hospital, participants were less willing to share their digital information with a pharmaceutical company or a digital technology company. Compared with research uses, they were less willing to share their information when it would be used for health care quality improvement or for marketing. Differences by disease were generally small, although participants were somewhat less willing to share digital information related to depression and diabetes compared with cancer and more willing to share digital information related to COVID-19.

Table 3 reports the results from a latent class analysis; 337 participants were universally opposed to sharing their digital data under any scenario and so were assigned to their own cluster. This cluster is not reported in Table 3 , as they were not formally included in the latent class analysis because they revealed no heterogeneity in response to varying conjoint attributes.

The most notable variation across the 3 clusters from the latent class analysis was in the model intercept, reflecting that the 3 derived clusters differed most in their general willingness to share information and that less difference was found among their views about specific uses, users, information types, or disease conditions. For that reason, the clusters were labeled as an averse cluster (1155 respondents [32.5%]), an uncertain cluster (1589 [44.8%]), and an agreeable cluster (462 [13.0%]), to which might be added a never cluster (337 [9.5%]), representing the participants unwilling to share any information.

The 3 clusters exhibited similar trends across the specific sharing scenarios. Among all 3 clusters, the importance weight was greatest for information type (averse: 53.1%; uncertain: 62.5%; agreeable: 63.1%). Individuals were least likely to share data reflecting personal finances (averse: coefficient, −0.46; 95% CI, −0.51 to −0.40; uncertain: coefficient, −0.85; 95% CI, −0.91 to −0.79; agreeable: coefficient, −0.32; 95% CI, −0.40 to −0.24) and places they visit from public cameras (averse: coefficient, −0.25; 95% CI, −0.31 to −0.20; uncertain: coefficient, −0.34; 95% CI, −0.39 to −0.28; agreeable: coefficient, −0.09; 95% CI, −0.17 to −0.02). Individuals were most likely to share data that reflected on their physical activity from apps on their phone (averse: coefficient, 0.14; 95% CI, 0.09-0.19; uncertain: coefficient, 0.34; 95% CI, 0.28-0.40; agreeable: coefficient, 0.11; 95% CI, 0.03-0.18). The Figure illustrates variation across 8 possible scenarios for the 3 clusters. Across-group variation was large, while within-group variation was largest for the uncertain group. For example, the agreeable group was most willing to share information from walking apps with a university hospital for research purposes (mean, 4.39; 95% CI, 4.32-4.46) compared with the uncertain (mean, 3.63; 95% CI, 3.58-3.67) and averse (mean, 2.16; 95% CI, 2.10-2.21) groups. Within the uncertain group, individuals were more willing to share information from walking apps with a university hospital for research purposes (mean, 3.63; 95% CI, 3.58-3.67) compared with financial information (mean, 2.44; 95% CI, 2.39-2.50).

Given the large variation in model intercepts in the latent class analysis, we also show results in Table 3 of an intercept-only model. This model reflects the mean ratings of respondents without regard for the assigned conjoint scenarios. Latent class analysis again results in 3 groups with similar numbers of respondents and intercepts to the full model (averse: mean rating, 1.64; 95% CI 1.62-1.65; uncertain: mean rating, 2.84, 95% CI, 2.81-2.86; agreeable: mean rating, 4.18, 95% CI, 4.16-4.21).

Table 4 shows the characteristics of 4 subgroups: the 3 clusters from the latent class analysis and the fourth group that was created given their universal opposition to sharing information under any situation. White participants and those with higher incomes were more likely to be in a more privacy-concerned cluster (averse and never clusters) than those with lower incomes and racial and ethnic minority participants. Those in fair or poor health were less likely to be in a more privacy-concerned cluster. Politically conservative respondents were more likely to be in a privacy-concerned cluster, particularly the group that was universally opposed to sharing (comprising 49.5% of that group [162 respondents] vs 32.6% of the overall study population [1136 respondents]).

This study has 3 main findings. First, consumers generally have views about privacy that shift only modestly based on context. Just more than half of respondents (55%) had preferences to share or not share their information that were largely independent of context: 10% were universally opposed to all sharing, 33% were opposed to most sharing, and 13% were in favor of most sharing. The remainder (45%) revealed preferences more responsive to the context of information reuse. Our findings are consistent with results from a 2019 survey by the Pew Research Center, 31 which found that many respondents were skeptical about sharing personal data but that support varied depending on the use (support for a range of health and nonhealth digital reuse scenarios varied between 25% and 49%, with 18%-27% of respondents with neutral views).

Second, the 4 privacy subgroups varied in their demographic composition. White and higher-income populations were more likely to be in a privacy-concerned subgroup (never or averse subgroup) than individuals from racial and ethnic minority populations or those from lower-income households. Prior research suggests that racial and ethnic minority populations and individuals from low-income households have greater concerns about digital privacy; however, these concerns often stem from concerns about specific harms from incidents related to identity theft or government surveillance. 32 , 33 Our study was more focused on nongovernmental uses of digital health information including nonprofit research uses as well as programs focused on health care quality or commercial health-related activities.

Third, although many respondents held strong views about whether they wished to share or not share their data, the specific context related to information reuse is still an important factor. Financial information, information from passive monitoring of location (from cameras), and monitoring chatter on social communication were evaluated as more sensitive. In contrast, information that currently has greater protection (ie, EHR data) or areas that have historically drawn the attention of ethicists and policy makers (ie, genetic data) were evaluated as less sensitive. With the lines between health and nonhealth information blurred more than ever, the traditional boundaries around different types of information may no longer be relevant.

The proliferation of digital health data from a wide range of sources creates many opportunities to develop programs and tools to improve health. Our study shows that many consumers—when given a choice—are reluctant to share their digital health information. Rather than context driving preferences, many consumers seemed to have strong views about sharing or not sharing information regardless of the specific use case scenario. The key question is whether enhanced privacy protections would increase trust and support for socially beneficial uses. Prior research suggests that when protections are perceived to be stronger (ie, consumers have greater control), consumers have fewer privacy concerns. 34 The current lack of protections may hinder consumer support for health programs powered by consumer digital data and data science.

Our study has limitations. First, because of the cross-sectional design, the findings represent a moment in time when the survey was administered (July 2020). Second, the findings represent the results of rating hypothetical scenarios rather than actual decisions. However, conjoint analysis is a rigorous and validated approach to measure preferences and predict real-world decisions. 25 , 26 Third, as with all survey research, nonresponse bias is a concern. However, the experimental design allows for strong internal validity from the conjoint experiment. In addition, the sample is drawn from a nationally representative panel population, and the participation rate among sampled individuals was high and similar to other published studies. 27 , 35

In this national survey study using conjoint analysis, we found that US adults held privacy views about their personal digital data that were partially informed by the context of use (ie, the specific use case scenario). However, these views were largely associated with participants’ underlying preferences about digital privacy overall.

Accepted for Publication: November 24, 2021.

Published: January 24, 2022. doi:10.1001/jamanetworkopen.2021.44787

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Grande D et al. JAMA Network Open .

Corresponding Author: David Grande, MD, MPA, Perelman School of Medicine, 3641 Locust Walk, Philadelphia PA 19104 ( [email protected] ).

Author Contributions: Dr Grande had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Grande, Iyengar, Merchant, Asch, Cannuscio.

Acquisition, analysis, or interpretation of data: Grande, Mitra, Iyengar, Merchant, Sharma, Cannuscio.

Drafting of the manuscript: Grande, Iyengar, Merchant, Sharma.

Critical revision of the manuscript for important intellectual content: Grande, Mitra, Iyengar, Merchant, Asch, Cannuscio.

Statistical analysis: Grande, Mitra, Iyengar.

Obtained funding: Grande, Cannuscio.

Administrative, technical, or material support: Grande, Merchant, Sharma.

Supervision: Mitra, Cannuscio.

Conflict of Interest Disclosures: Dr Asch reported being a partner and part owner of VAL Health. No other disclosures were reported.

Funding/Support: This research was supported by grant 5R01HG009655-04 from the National Human Genome Research Institute/National Institutes of Health.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

• Register for email alerts with links to free full-text articles
• Access PDFs of free articles
• Manage your interests
• Save searches and receive search alerts

Artificial intelligence in strategy

Can machines automate strategy development? The short answer is no. However, there are numerous aspects of strategists’ work where AI and advanced analytics tools can already bring enormous value. Yuval Atsmon is a senior partner who leads the new McKinsey Center for Strategy Innovation, which studies ways new technologies can augment the timeless principles of strategy. In this episode of the Inside the Strategy Room podcast, he explains how artificial intelligence is already transforming strategy and what’s on the horizon. This is an edited transcript of the discussion. For more conversations on the strategy issues that matter, follow the series on your preferred podcast platform .

Joanna Pachner: What does artificial intelligence mean in the context of strategy?

Yuval Atsmon: When people talk about artificial intelligence, they include everything to do with analytics, automation, and data analysis. Marvin Minsky, the pioneer of artificial intelligence research in the 1960s, talked about AI as a “suitcase word”—a term into which you can stuff whatever you want—and that still seems to be the case. We are comfortable with that because we think companies should use all the capabilities of more traditional analysis while increasing automation in strategy that can free up management or analyst time and, gradually, introducing tools that can augment human thinking.

Joanna Pachner: AI has been embraced by many business functions, but strategy seems to be largely immune to its charms. Why do you think that is?

Subscribe to the Inside the Strategy Room podcast

Yuval Atsmon: You’re right about the limited adoption. Only 7 percent of respondents to our survey about the use of AI say they use it in strategy or even financial planning, whereas in areas like marketing, supply chain, and service operations, it’s 25 or 30 percent. One reason adoption is lagging is that strategy is one of the most integrative conceptual practices. When executives think about strategy automation, many are looking too far ahead—at AI capabilities that would decide, in place of the business leader, what the right strategy is. They are missing opportunities to use AI in the building blocks of strategy that could significantly improve outcomes.

I like to use the analogy to virtual assistants. Many of us use Alexa or Siri but very few people use these tools to do more than dictate a text message or shut off the lights. We don’t feel comfortable with the technology’s ability to understand the context in more sophisticated applications. AI in strategy is similar: it’s hard for AI to know everything an executive knows, but it can help executives with certain tasks.

When executives think about strategy automation, many are looking too far ahead—at AI deciding the right strategy. They are missing opportunities to use AI in the building blocks of strategy.

Joanna Pachner: What kind of tasks can AI help strategists execute today?

Yuval Atsmon: We talk about six stages of AI development. The earliest is simple analytics, which we refer to as descriptive intelligence. Companies use dashboards for competitive analysis or to study performance in different parts of the business that are automatically updated. Some have interactive capabilities for refinement and testing.

The second level is diagnostic intelligence, which is the ability to look backward at the business and understand root causes and drivers of performance. The level after that is predictive intelligence: being able to anticipate certain scenarios or options and the value of things in the future based on momentum from the past as well as signals picked in the market. Both diagnostics and prediction are areas that AI can greatly improve today. The tools can augment executives’ analysis and become areas where you develop capabilities. For example, on diagnostic intelligence, you can organize your portfolio into segments to understand granularly where performance is coming from and do it in a much more continuous way than analysts could. You can try 20 different ways in an hour versus deploying one hundred analysts to tackle the problem.

Predictive AI is both more difficult and more risky. Executives shouldn’t fully rely on predictive AI, but it provides another systematic viewpoint in the room. Because strategic decisions have significant consequences, a key consideration is to use AI transparently in the sense of understanding why it is making a certain prediction and what extrapolations it is making from which information. You can then assess if you trust the prediction or not. You can even use AI to track the evolution of the assumptions for that prediction.

Those are the levels available today. The next three levels will take time to develop. There are some early examples of AI advising actions for executives’ consideration that would be value-creating based on the analysis. From there, you go to delegating certain decision authority to AI, with constraints and supervision. Eventually, there is the point where fully autonomous AI analyzes and decides with no human interaction.

Because strategic decisions have significant consequences, you need to understand why AI is making a certain prediction and what extrapolations it’s making from which information.

Joanna Pachner: What kind of businesses or industries could gain the greatest benefits from embracing AI at its current level of sophistication?

Yuval Atsmon: Every business probably has some opportunity to use AI more than it does today. The first thing to look at is the availability of data. Do you have performance data that can be organized in a systematic way? Companies that have deep data on their portfolios down to business line, SKU, inventory, and raw ingredients have the biggest opportunities to use machines to gain granular insights that humans could not.

Companies whose strategies rely on a few big decisions with limited data would get less from AI. Likewise, those facing a lot of volatility and vulnerability to external events would benefit less than companies with controlled and systematic portfolios, although they could deploy AI to better predict those external events and identify what they can and cannot control.

Third, the velocity of decisions matters. Most companies develop strategies every three to five years, which then become annual budgets. If you think about strategy in that way, the role of AI is relatively limited other than potentially accelerating analyses that are inputs into the strategy. However, some companies regularly revisit big decisions they made based on assumptions about the world that may have since changed, affecting the projected ROI of initiatives. Such shifts would affect how you deploy talent and executive time, how you spend money and focus sales efforts, and AI can be valuable in guiding that. The value of AI is even bigger when you can make decisions close to the time of deploying resources, because AI can signal that your previous assumptions have changed from when you made your plan.

Joanna Pachner: Can you provide any examples of companies employing AI to address specific strategic challenges?

Yuval Atsmon: Some of the most innovative users of AI, not coincidentally, are AI- and digital-native companies. Some of these companies have seen massive benefits from AI and have increased its usage in other areas of the business. One mobility player adjusts its financial planning based on pricing patterns it observes in the market. Its business has relatively high flexibility to demand but less so to supply, so the company uses AI to continuously signal back when pricing dynamics are trending in a way that would affect profitability or where demand is rising. This allows the company to quickly react to create more capacity because its profitability is highly sensitive to keeping demand and supply in equilibrium.

Joanna Pachner: Given how quickly things change today, doesn’t AI seem to be more a tactical than a strategic tool, providing time-sensitive input on isolated elements of strategy?

Yuval Atsmon: It’s interesting that you make the distinction between strategic and tactical. Of course, every decision can be broken down into smaller ones, and where AI can be affordably used in strategy today is for building blocks of the strategy. It might feel tactical, but it can make a massive difference. One of the world’s leading investment firms, for example, has started to use AI to scan for certain patterns rather than scanning individual companies directly. AI looks for consumer mobile usage that suggests a company’s technology is catching on quickly, giving the firm an opportunity to invest in that company before others do. That created a significant strategic edge for them, even though the tool itself may be relatively tactical.

Joanna Pachner: McKinsey has written a lot about cognitive biases  and social dynamics that can skew decision making. Can AI help with these challenges?

Yuval Atsmon: When we talk to executives about using AI in strategy development, the first reaction we get is, “Those are really big decisions; what if AI gets them wrong?” The first answer is that humans also get them wrong—a lot. [Amos] Tversky, [Daniel] Kahneman, and others have proven that some of those errors are systemic, observable, and predictable. The first thing AI can do is spot situations likely to give rise to biases. For example, imagine that AI is listening in on a strategy session where the CEO proposes something and everyone says “Aye” without debate and discussion. AI could inform the room, “We might have a sunflower bias here,” which could trigger more conversation and remind the CEO that it’s in their own interest to encourage some devil’s advocacy.

We also often see confirmation bias, where people focus their analysis on proving the wisdom of what they already want to do, as opposed to looking for a fact-based reality. Just having AI perform a default analysis that doesn’t aim to satisfy the boss is useful, and the team can then try to understand why that is different than the management hypothesis, triggering a much richer debate.

In terms of social dynamics, agency problems can create conflicts of interest. Every business unit [BU] leader thinks that their BU should get the most resources and will deliver the most value, or at least they feel they should advocate for their business. AI provides a neutral way based on systematic data to manage those debates. It’s also useful for executives with decision authority, since we all know that short-term pressures and the need to make the quarterly and annual numbers lead people to make different decisions on the 31st of December than they do on January 1st or October 1st. Like the story of Ulysses and the sirens, you can use AI to remind you that you wanted something different three months earlier. The CEO still decides; AI can just provide that extra nudge.

Joanna Pachner: It’s like you have Spock next to you, who is dispassionate and purely analytical.

Yuval Atsmon: That is not a bad analogy—for Star Trek fans anyway.

Joanna Pachner: Do you have a favorite application of AI in strategy?

Yuval Atsmon: I have worked a lot on resource allocation, and one of the challenges, which we call the hockey stick phenomenon, is that executives are always overly optimistic about what will happen. They know that resource allocation will inevitably be defined by what you believe about the future, not necessarily by past performance. AI can provide an objective prediction of performance starting from a default momentum case: based on everything that happened in the past and some indicators about the future, what is the forecast of performance if we do nothing? This is before we say, “But I will hire these people and develop this new product and improve my marketing”— things that every executive thinks will help them overdeliver relative to the past. The neutral momentum case, which AI can calculate in a cold, Spock-like manner, can change the dynamics of the resource allocation discussion. It’s a form of predictive intelligence accessible today and while it’s not meant to be definitive, it provides a basis for better decisions.

Joanna Pachner: Do you see access to technology talent as one of the obstacles to the adoption of AI in strategy, especially at large companies?

Yuval Atsmon: I would make a distinction. If you mean machine-learning and data science talent or software engineers who build the digital tools, they are definitely not easy to get. However, companies can increasingly use platforms that provide access to AI tools and require less from individual companies. Also, this domain of strategy is exciting—it’s cutting-edge, so it’s probably easier to get technology talent for that than it might be for manufacturing work.

The bigger challenge, ironically, is finding strategists or people with business expertise to contribute to the effort. You will not solve strategy problems with AI without the involvement of people who understand the customer experience and what you are trying to achieve. Those who know best, like senior executives, don’t have time to be product managers for the AI team. An even bigger constraint is that, in some cases, you are asking people to get involved in an initiative that may make their jobs less important. There could be plenty of opportunities for incorpo­rating AI into existing jobs, but it’s something companies need to reflect on. The best approach may be to create a digital factory where a different team tests and builds AI applications, with oversight from senior stakeholders.

The big challenge is finding strategists to contribute to the AI effort. You are asking people to get involved in an initiative that may make their jobs less important.

Joanna Pachner: Do you think this worry about job security and the potential that AI will automate strategy is realistic?

Yuval Atsmon: The question of whether AI will replace human judgment and put humanity out of its job is a big one that I would leave for other experts.

The pertinent question is shorter-term automation. Because of its complexity, strategy would be one of the later domains to be affected by automation, but we are seeing it in many other domains. However, the trend for more than two hundred years has been that automation creates new jobs, although ones requiring different skills. That doesn’t take away the fear some people have of a machine exposing their mistakes or doing their job better than they do it.

Joanna Pachner: We recently published an article about strategic courage in an age of volatility  that talked about three types of edge business leaders need to develop. One of them is an edge in insights. Do you think AI has a role to play in furnishing a proprietary insight edge?

Yuval Atsmon: One of the challenges most strategists face is the overwhelming complexity of the world we operate in—the number of unknowns, the information overload. At one level, it may seem that AI will provide another layer of complexity. In reality, it can be a sharp knife that cuts through some of the clutter. The question to ask is, Can AI simplify my life by giving me sharper, more timely insights more easily?

Joanna Pachner: You have been working in strategy for a long time. What sparked your interest in exploring this intersection of strategy and new technology?

Yuval Atsmon: I have always been intrigued by things at the boundaries of what seems possible. Science fiction writer Arthur C. Clarke’s second law is that to discover the limits of the possible, you have to venture a little past them into the impossible, and I find that particularly alluring in this arena.

AI in strategy is in very nascent stages but could be very consequential for companies and for the profession. For a top executive, strategic decisions are the biggest way to influence the business, other than maybe building the top team, and it is amazing how little technology is leveraged in that process today. It’s conceivable that competitive advantage will increasingly rest in having executives who know how to apply AI well. In some domains, like investment, that is already happening, and the difference in returns can be staggering. I find helping companies be part of that evolution very exciting.

Explore a career with us

Related articles.

Strategic courage in an age of volatility

Bias Busters Collection